Reported protocols for the differentiation of human PSCs toward cerebellar neurons.
\\n\\n
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Barely three months into the new year and we are happy to announce a monumental milestone reached - 150 million downloads.
\n\nThis achievement solidifies IntechOpen’s place as a pioneer in Open Access publishing and the home to some of the most relevant scientific research available through Open Access.
\n\nWe are so proud to have worked with so many bright minds throughout the years who have helped us spread knowledge through the power of Open Access and we look forward to continuing to support some of the greatest thinkers of our day.
\n\nThank you for making IntechOpen your place of learning, sharing, and discovery, and here’s to 150 million more!
\n\n\n\n\n'}],latestNews:[{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"}]},book:{item:{type:"book",id:"9872",leadTitle:null,fullTitle:"Models and Technologies for Smart, Sustainable and Safe Transportation Systems",title:"Models and Technologies for Smart, Sustainable and Safe Transportation Systems",subtitle:null,reviewType:"peer-reviewed",abstract:"Innovative and smart mobility systems are expected to make transportation systems more sustainable, inclusive, and safe. Because of changing mobility paradigms, transport planning and design require different methodological approaches. Over twelve chapters, this book examines and analyzes Mobility as a Service (MaaS), travel behavior, traffic control, intelligent transportation system design, electric, connected, and automated vehicles, and much more.",isbn:"978-1-83880-823-5",printIsbn:"978-1-83880-802-0",pdfIsbn:"978-1-83880-824-2",doi:"10.5772/intechopen.87681",price:119,priceEur:129,priceUsd:155,slug:"models-and-technologies-for-smart-sustainable-and-safe-transportation-systems",numberOfPages:266,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"ef80dab7f0350ea7cb28f40eedea2b35",bookSignature:"Stefano de Luca, Roberta Di Pace and Chiara Fiori",publishedDate:"July 28th 2021",coverURL:"https://cdn.intechopen.com/books/images_new/9872.jpg",numberOfDownloads:4736,numberOfWosCitations:0,numberOfCrossrefCitations:6,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:9,numberOfDimensionsCitationsByBook:0,hasAltmetrics:0,numberOfTotalCitations:15,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"May 6th 2020",dateEndSecondStepPublish:"May 27th 2020",dateEndThirdStepPublish:"July 26th 2020",dateEndFourthStepPublish:"October 14th 2020",dateEndFifthStepPublish:"December 13th 2020",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"271061",title:"Prof.",name:"Stefano",middleName:null,surname:"de Luca",slug:"stefano-de-luca",fullName:"Stefano de Luca",profilePictureURL:"https://mts.intechopen.com/storage/users/271061/images/system/271061.jpeg",biography:"Stefano de Luca is a Full Professor of Transportation Planning and Transportation Systems Theory, at the University of Salerno, Italy, where he is also the director of the Transportation Systems Analysis Laboratory and rector’s delegate to Transport and Mobility. His research includes transportation planning techniques, choice modelling, signal settings design, traffic assignment models and algorithms, and freight/passenger terminal simulation and optimization. He serves on the editorial advisory board for the Journal of Advanced Transportation and Sustainability. He has authored more than 100 book chapters and journal articles, and is a consultant for the Italian Ministry of Transportation, the Transport Commission of Campania Region, and the Salerno and Avellino Transportation Departments. He is a member of IEEE Intelligent Transportation Systems Society, the Italian Association of Transport Academicians, and the Italian Transport Policy Society.",institutionString:"University of Salerno",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"3",institution:{name:"University of Salerno",institutionURL:null,country:{name:"Italy"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:{id:"271713",title:"Dr.",name:"Roberta",middleName:null,surname:"Di Pace",slug:"roberta-di-pace",fullName:"Roberta Di Pace",profilePictureURL:"https://mts.intechopen.com/storage/users/271713/images/system/271713.jpeg",biography:"Roberta Di Pace received an MSc and Ph.D. in Transportation Engineering from the University of Naples “Federico II,” Italy, in 2005 and 2009, respectively. She is a Professor of Transportation Engineering, Department of Civil Engineering, University of Salerno, Italy. She is also an aggregate professor of Technique and Transport Economics and Transportation Systems Design. Since 2010 she has been a member of the Transportation Planning and Modelling Laboratory. Her main research fields include the development of analytical tools for advanced traveler information systems, traffic flow modelling, network signal setting design, and advanced traffic management systems. She is a member of IEEE Intelligent Transportation Systems Society and IEEE Women in Engineering.",institutionString:"University of Salerno",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"University of Salerno",institutionURL:null,country:{name:"Italy"}}},coeditorTwo:{id:"321783",title:"Dr.",name:"Chiara",middleName:null,surname:"Fiori",slug:"chiara-fiori",fullName:"Chiara Fiori",profilePictureURL:"https://mts.intechopen.com/storage/users/321783/images/system/321783.jpg",biography:"Chiara Fiori is an assistant professor, at the Department of Civil Engineering, University of Salerno, Italy.\n\nShe has international research experience as a visiting scientist at MobiLab Transport Research Group, University of Luxembourg (2020–2021); the European Commission, Joint Research Center, Directorate for Energy, Transport and Climate Change (2017–2018); the Virginia Tech Transportation Institute, USA (2015–2016); and others. Her research interests include testing, modeling, and simulation of electrified vehicles powertrains (for personal, public, and freight mobility) and their impact assessment on eco-routing and electric power grids; integration of microscopic traffic flow and energy consumption models and effects on traffic control systems; well-to-wheels analysis; modeling, simulation, and impact assessment of port operations in urban contexts; and Multi-Vehicle Dynamic Traffic Assignment.",institutionString:"University of Salerno",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"University of Salerno",institutionURL:null,country:{name:"Italy"}}},coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"713",title:"Transportation Engineering",slug:"engineering-civil-engineering-transportation-engineering"}],chapters:[{id:"77146",title:"Adaptive Travel Mode Choice in the Era of Mobility as a Service (MaaS): Literature Review and the Hypermode Mode Choice Paradigm",doi:"10.5772/intechopen.98432",slug:"adaptive-travel-mode-choice-in-the-era-of-mobility-as-a-service-maas-literature-review-and-the-hyper",totalDownloads:321,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Mobility as a Service (MaaS) is becoming a “fashionable” solution to increase transport users’ satisfaction and accessibility, by providing new services obtained by optimally integrating sustainable modes, but also guaranteeing mass transport and less sustainable modes, guaranteeing fast and lean access/egress to the mass transport. In this context, the understanding and prediction of travellers’ mode choices is crucial not only for the effective management of multimodal transport networks, but also successful implementation of new transport schemes. Traditional studies on mode choices typically treat travellers’ decision-making processes as planned behaviour. However, this approach is now challenged by the widely distributed, multi-sourced, and heterogeneous travel information made available in real time through information and communication technologies (ICT), especially in the presence of a variety of available mode options in dense urban areas. Some of the real-time factors that affect mode choices include availability of shared vehicles, real-time passenger information, unexpected disruptions, and weather. These real-time factors are insufficiently captured by existing mode choice models. This chapter aims to propose an introduction to MaaS, a literature review on mode choice paradigms, then it proposes a novel behavioural concept referred to as the hypermode. It will be illustrated a two-level mode choice decision architecture, which captures the influence of real-time events and travellers’ adaptive behaviour. A pilot survey shows the relevance of some real-time factors, and corroborates the hypothesized adaptive mode choice behaviour in both recurrent and occasional trip scenarios.",signatures:"Stefano de Luca and Margherita Mascia",downloadPdfUrl:"/chapter/pdf-download/77146",previewPdfUrl:"/chapter/pdf-preview/77146",authors:[{id:"271061",title:"Prof.",name:"Stefano",surname:"de Luca",slug:"stefano-de-luca",fullName:"Stefano de Luca"},{id:"419371",title:"Dr.",name:"Margherita",surname:"Mascia",slug:"margherita-mascia",fullName:"Margherita Mascia"}],corrections:null},{id:"73240",title:"Recent Progress in Activity-Based Travel Demand Modeling: Rising Data and Applicability",doi:"10.5772/intechopen.93827",slug:"recent-progress-in-activity-based-travel-demand-modeling-rising-data-and-applicability",totalDownloads:718,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Over 30 years have passed since activity-based travel demand models (ABMs) emerged to overcome the limitations of the preceding models which have dominated the field for over 50 years. Activity-based models are valuable tools for transportation planning and analysis, detailing the tour and mode-restricted nature of the household and individual travel choices. Nevertheless, no single approach has emerged as a dominant method, and research continues to improve ABM features to make them more accurate, robust, and practical. This paper describes the state of art and practice, including the ongoing ABM research covering both demand and supply considerations. Despite the substantial developments, ABM’s abilities in reflecting behavioral realism are still limited. Possible solutions to address this issue include increasing the inaccuracy of the primary data, improved integrity of ABMs across days of the week, and tackling the uncertainty via integrating demand and supply. Opportunities exist to test, the feasibility of spatial transferability of ABMs to new geographical contexts along with expanding the applicability of ABMs in transportation policy-making.",signatures:"Atousa Tajaddini, Geoffrey Rose, Kara M. Kockelman and Hai L. Vu",downloadPdfUrl:"/chapter/pdf-download/73240",previewPdfUrl:"/chapter/pdf-preview/73240",authors:[{id:"321573",title:"Prof.",name:"Hai L.",surname:"Vu",slug:"hai-l.-vu",fullName:"Hai L. Vu"},{id:"327536",title:"Ms.",name:"Atousa",surname:"Tajaddini",slug:"atousa-tajaddini",fullName:"Atousa Tajaddini"},{id:"327537",title:"Prof.",name:"Geoffrey",surname:"Rose",slug:"geoffrey-rose",fullName:"Geoffrey Rose"},{id:"327538",title:"Prof.",name:"Kara M.",surname:"Kockelman",slug:"kara-m.-kockelman",fullName:"Kara M. Kockelman"}],corrections:null},{id:"74201",title:"Attitudes and Behaviours in Relation to New Technology in Transport and the Take-Up amongst Older Travellers",doi:"10.5772/intechopen.94963",slug:"attitudes-and-behaviours-in-relation-to-new-technology-in-transport-and-the-take-up-amongst-older-tr",totalDownloads:253,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:1,abstract:"Numbers of older people are increasing and this will continue for several decades to come. With that, there are changes as we age that can affect or impact upon our travelling and transportation needs and behaviour. In addition, there is an almost universal problem that many of all ages people have low levels of computer literacy. Transport may well look very different in the future. Not only automated vehicles, but also new transportation systems, such as Mobility as a Service [MaaS] and the likely developments in public transport that incorporate real time travel information, facilities and ease of use information all mean that older people wishing to travel will necessarily have to engage with some forms of new technology. The new systems will need to be personalisable to individual travellers. This chapter considers the needs of older travellers and how new technology can meet some of those needs and what is necessary for it to be appropriate to, and usable by, older travellers.",signatures:"Joan Harvey",downloadPdfUrl:"/chapter/pdf-download/74201",previewPdfUrl:"/chapter/pdf-preview/74201",authors:[{id:"322228",title:"Dr.",name:"Joan",surname:"Harvey",slug:"joan-harvey",fullName:"Joan Harvey"}],corrections:null},{id:"75272",title:"Cognitive Profile of Optimistic Offender Drivers Affected by Psychological Interventions for a Sustainable and Safer Driving’s Behavior",doi:"10.5772/intechopen.96249",slug:"cognitive-profile-of-optimistic-offender-drivers-affected-by-psychological-interventions-for-a-susta",totalDownloads:247,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:1,abstract:"An empirically verified fact is that the majority of traffic accidents occur as a result of risky behaviours that drivers assume, more or less, voluntarily. Drivers are not aware of the perception of risk and the subjective perception of control that we believe we have. We have delimitated the characteristics of a group of optimistic offender drivers, which reveal, on the hand, a great lack of understanding of the true impact that external factors can have on driving and; on the other hand, they tend to overestimate their abilities and overconfident in their ability to avoid accidents. In addition, these drivers do not usually experience negative emotions when they fail. All this, together is what increases the probability of suffering an accident. The consideration of the different cognitive profiles in the perception of the risk or challenge when facing potential traffic situations may provide us with a better understanding of the true nature of offending drivers. The need to carry out experimental studies using new assessment instruments (i.e. Eye tracking, Bio-Feedback, evoked potentials, etc.) can facilitate a better understanding of the cognitive processes that explain the attitudes and behaviors of drivers; and therefore, achieve a lower rate of car accidents.",signatures:"Carlos Hugo Criado del Valle and Parichehr Scharifi",downloadPdfUrl:"/chapter/pdf-download/75272",previewPdfUrl:"/chapter/pdf-preview/75272",authors:[{id:"321052",title:"Prof.",name:"Carlos Hugo",surname:"Criado del Valle",slug:"carlos-hugo-criado-del-valle",fullName:"Carlos Hugo Criado del Valle"},{id:"339076",title:"Dr.",name:"Parichehr",surname:"Scharifi",slug:"parichehr-scharifi",fullName:"Parichehr Scharifi"}],corrections:null},{id:"73356",title:"Optimal Management of Electrified and Cooperative Bus Systems",doi:"10.5772/intechopen.93892",slug:"optimal-management-of-electrified-and-cooperative-bus-systems",totalDownloads:355,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"This chapter presents an integrated management approach exploiting the potentials of the new Cooperative Intelligent Transportation Systems (C-ITS) to meet the requirements of the next generation Public Transport (PT). This approach considers the additional complexity of electrification—for instance electric busses need to periodically recharge during operation using dedicated infrastructure. This not only can impact service level, but also extend operating costs with complex electric charges. We develop new strategies explicitly optimizing the interactions within the PT ecosystem consisting of vehicles, traffic signals, and e-bus charging infrastructure. To achieve these goals, we rely on vehicle control rather than on the use of transit signal priority, which in congested urban scenarios can have negative effects on overall traffic performance. The main research challenges are in formulating and solving complex multi-objective optimization problems and real-time control. The proposed system is tested and evaluated in simulation showing the benefits of electrified and cooperative bus systems.",signatures:"Francesco Viti, Marco Rinaldi and Georgios Laskaris",downloadPdfUrl:"/chapter/pdf-download/73356",previewPdfUrl:"/chapter/pdf-preview/73356",authors:[{id:"321907",title:"Dr.",name:"Francesco",surname:"Viti",slug:"francesco-viti",fullName:"Francesco Viti"},{id:"328609",title:"Dr.",name:"Marco",surname:"Rinaldi",slug:"marco-rinaldi",fullName:"Marco Rinaldi"},{id:"328610",title:"Dr.",name:"Georgios",surname:"Laskaris",slug:"georgios-laskaris",fullName:"Georgios Laskaris"}],corrections:null},{id:"73973",title:"Models and Methods for Intelligent Highway Routing of Human-Driven and Connected-and-Automated Vehicles",doi:"10.5772/intechopen.94332",slug:"models-and-methods-for-intelligent-highway-routing-of-human-driven-and-connected-and-automated-vehic",totalDownloads:318,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Connected and automated vehicles (CAVs) have seen a rapid surge in interest over the past few years. A lot of focus is being placed on improving the efficiency and robustness of transportation systems by leveraging the sensors and capabilities of CAVs. However, the integration of CAVs into existing traffic infrastructure would give rise to certain issues that must be addressed before the CAVs can be seen ubiquitously on public roads. Since the highway networks are considered permanent investments that are expensive to build and maintain, the priority is to improve the efficiency of the current traffic system. This chapter explores the integration of two of the most common traffic management strategies, namely, ramp metering (RM) and route guidance (RG), into existing highway networks with human-driven vehicles (HDVs). The introduction of CAVs to public roads will engender issues pertaining to safe interactions between CAVs and HDVs. The later part of the chapter addresses the specific problems of improving highway on-ramp merging efficiency by optimally coordinating CAVs. The chapter concludes by presenting a scenario that requires an explicit consideration of interactions between HDVs and CAVs.",signatures:"Fatemeh Alimardani, Nilesh Suriyarachchi, Faizan M. Tariq and John S. Baras",downloadPdfUrl:"/chapter/pdf-download/73973",previewPdfUrl:"/chapter/pdf-preview/73973",authors:[{id:"321946",title:"Prof.",name:"John",surname:"Baras",slug:"john-baras",fullName:"John Baras"},{id:"327562",title:"Ph.D.",name:"Faizan",surname:"Tariq",slug:"faizan-tariq",fullName:"Faizan Tariq"},{id:"329011",title:"Mrs.",name:"Fatemeh",surname:"Alimardani",slug:"fatemeh-alimardani",fullName:"Fatemeh Alimardani"},{id:"329012",title:"Dr.",name:"Nilesh",surname:"Suriyarachchi",slug:"nilesh-suriyarachchi",fullName:"Nilesh Suriyarachchi"}],corrections:null},{id:"74412",title:"Centralised Traffic Control and Green Light Optimal Speed Advisory Procedure in Mixed Traffic Flow: An Integrated Modelling Framework",doi:"10.5772/intechopen.95247",slug:"centralised-traffic-control-and-green-light-optimal-speed-advisory-procedure-in-mixed-traffic-flow-a",totalDownloads:374,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"The paper aims to develop an integrated modelling framework for urban network traffic control in the presence of connected and autonomous vehicles (CAVs). The framework is further composed of two sub models: the first of which focuses on the traffic control problem in the case of hybrid flow conditions (unequipped vehicles and connected vehicles) and the second aims to control the automated vehicles in terms of speed optimisation. The traffic control strategy drew on the hybrid combination between the centralised approach based on a multi-objective optimisation and a link metering based on a single control function; whilst with reference to the speed guidance, the GLOSA (Green Light Optimal Speed Advisory) procedure was considered. Furthermore, the presence of connected vehicles has also been considered to support the estimation procedure of location and speed of unequipped vehicles. In terms of traffic flow modelling the microscopic approach has been applied. The proposed framework was applied by considering a simple real network (in the city centre of Naples, in the Southern of Italy) that was composed by one origin–destination pair and two alternative paths. The network layout is characterised by one diversion node and two alternative paths connecting the same origin - destination pair; three scenarios were tested: the first was only based on a centralised traffic control procedure, the second on speed guidance optimisation and the third was based on the combination of both sub-models. Finally, the framework effectiveness was analised in terms of within-day dynamics with respect to the travel times and queue length performance indices.",signatures:"Roberta Di Pace, Chiara Fiori, Luigi Pariota and Facundo Storani",downloadPdfUrl:"/chapter/pdf-download/74412",previewPdfUrl:"/chapter/pdf-preview/74412",authors:[{id:"271713",title:"Dr.",name:"Roberta",surname:"Di Pace",slug:"roberta-di-pace",fullName:"Roberta Di Pace"},{id:"321783",title:"Dr.",name:"Chiara",surname:"Fiori",slug:"chiara-fiori",fullName:"Chiara Fiori"},{id:"271714",title:"Dr.",name:"Facundo",surname:"Storani",slug:"facundo-storani",fullName:"Facundo Storani"},{id:"333732",title:"Dr.",name:"Luigi",surname:"Pariota",slug:"luigi-pariota",fullName:"Luigi Pariota"}],corrections:null},{id:"73941",title:"Towards Shared Mobility Services in Ring Shape",doi:"10.5772/intechopen.94410",slug:"towards-shared-mobility-services-in-ring-shape",totalDownloads:367,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"A shared mobility service (SMS) under ring shape would combine the principle of service cycle along a fixed route (as in a transit line) and a fairly important territorial coverage, assuming that every user would accept to walk on some length to and from the service. Thus, service availability can be optimised, detours are avoided, vehicles achieve higher productivity. The synergy between the ring-shaped infrastructure and the vehicle fleet enables to optimise the quality of service in terms of access time and ride time, and also to reduce production costs - and therefore the tariff fares, under suitable regulation. The chapter aims to reveal these ‘systemic qualities’ of ring-shaped SMSs by providing a mathematical model called ‘Orbicity’. It has a four-fold architecture: (i) traffic operations, (ii) supply-demand equilibrium under elastic demand, (iii) service management with endogenous fleet size and fare rate, (iv) service policy in terms of technology (vehicle type, number of places, energy vector, driving technology) and also the regulation regime. After outlining the model for ring-shaped shuttle services, we explore a set of scenarios along two axes of technological generation and regulation regime. It appears that ring-shaped shuttle services could be supplied at very affordable prices, while achieving profitability and requiring no public subsidies.",signatures:"Fabien Leurent",downloadPdfUrl:"/chapter/pdf-download/73941",previewPdfUrl:"/chapter/pdf-preview/73941",authors:[{id:"321878",title:"Prof.",name:"Fabien",surname:"Leurent",slug:"fabien-leurent",fullName:"Fabien Leurent"}],corrections:null},{id:"74333",title:"Transit Signal Priority in Smart Cities",doi:"10.5772/intechopen.94742",slug:"transit-signal-priority-in-smart-cities",totalDownloads:449,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Giving priority to public transport vehicles at traffic signals is one of the traffic management strategies deployed at emerging smart cities to increase the quality of service for public transit users. It is a key to breaking the vicious cycle of congestion that threatens to bring cities into gridlock. In that cycle, increasing private traffic makes public transport become slower, less reliable, and less attractive. This results in deteriorated transit speed and reliability and induces more people to leave public transit in favor of the private cars, which create more traffic congestion, generate emissions, and increase energy consumption. Prioritizing public transit would break the vicious cycle and make it a more attractive mode as traffic demand and urban networks grow. A traditional way of protecting public transit from congestion is to move it either underground or above ground, as in the form of a metro/subway or air rail or create a dedicated lane as in the form of bus lane or light rail transit (LRT). However, due to the enormous capital expense involved or the lack of right-of-way, these solutions are often limited to few travel corridors or where money is not an issue. An alternative to prioritizing space to transit is to prioritize transit through time in the form of Transit Signal Priority (TSP). Noteworthy, transit and specifically bus schedules are known to be unstable and can be thrown off their schedule with even small changes in traffic or dwell time. At the same time, transit service reliability is an important factor for passengers and transit agencies. Less variability in transit travel time will need less slack or layover time. Thus, transit schedulers are interested in reducing transit travel time and its variability. One way to reach this goal is through an active intervention like TSP. In this chapter a comprehensive review of transit signal priority models is presented. The studies are classified into different categories which are: signal priority and different control systems, passive versus active priority, predictive transit signal priority, priority with connected vehicles, multi-modal signal priority models, and other practical considerations.",signatures:"Bahman Moghimi and Camille Kamga",downloadPdfUrl:"/chapter/pdf-download/74333",previewPdfUrl:"/chapter/pdf-preview/74333",authors:[{id:"321370",title:"Dr.",name:"Bahman",surname:"Moghimi",slug:"bahman-moghimi",fullName:"Bahman Moghimi"},{id:"340958",title:"Prof.",name:"Camille",surname:"Kamga",slug:"camille-kamga",fullName:"Camille Kamga"}],corrections:null},{id:"73595",title:"Advanced Vehicles: Challenges for Transportation Systems Engineering",doi:"10.5772/intechopen.94105",slug:"advanced-vehicles-challenges-for-transportation-systems-engineering",totalDownloads:178,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Automatic vehicles represent one of the most active research fields within engineering. Among transportation systems engineering research topics, we highlight the need to update and/or develop new mathematical models, computer science methods and electronic technologies that contribute to the development of more effective, accurate and robust tools. In order to develop more effective models, it is advisable to consider the opportunity to interact with other specialists from sectors different of the transportation systems engineering to provide solutions to problems that may arise during the modeling and further new points of view. The main goal of this paper is discussing the most likely positive and negative effects of mixed flow expected in the near future, analyzing the main classifying criteria such as ownership, on-board technologies (sensor), and reviewing the most effective tools already available for macroscopic analysis of multi vehicle type transportation systems.",signatures:"Orlando Giannattasio and Giulio E. Cantarella",downloadPdfUrl:"/chapter/pdf-download/73595",previewPdfUrl:"/chapter/pdf-preview/73595",authors:[{id:"323423",title:"Ph.D.",name:"Orlando",surname:"Giannattasio",slug:"orlando-giannattasio",fullName:"Orlando Giannattasio"},{id:"331140",title:"Prof.",name:"Giulio Erberto",surname:"Cantarella",slug:"giulio-erberto-cantarella",fullName:"Giulio Erberto Cantarella"}],corrections:null},{id:"73821",title:"Driver Assistance Technologies",doi:"10.5772/intechopen.94354",slug:"driver-assistance-technologies",totalDownloads:601,totalCrossrefCites:0,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Topic: Driver Assistance Technology is emerging as new driving technology popularly known as ADAS. It is supported with Adaptive Cruise Control, Automatic Emergency Brake, blind spot monitoring, lane change assistance, and forward collision warnings etc. It is an important platform to integrate these multiple applications by using data from multifunction sensors, cameras, radars, lidars etc. and send command to plural actuators, engine, brake, steering etc. ADAS technology can detect some objects, do basic classification, alert the driver of hazardous road conditions, and in some cases, slow or stop the vehicle. The architecture of the electronic control units (ECUs) is responsible for executing advanced driver assistance systems (ADAS) in vehicle which is changing as per its response during the process of driving. Automotive system architecture integrates multiple applications into ADAS ECUs that serve multiple sensors for their functions. Hardware architecture of ADAS and autonomous driving, includes automotive Ethernet, TSN, Ethernet switch and gateway, and domain controller while Software architecture of ADAS and autonomous driving, including AUTOSAR Classic and Adaptive, ROS 2.0 and QNX. This chapter explains the functioning of Assistance Driving Technology with the help of its architecture and various types of sensors.",signatures:"Pradip Kumar Sarkar",downloadPdfUrl:"/chapter/pdf-download/73821",previewPdfUrl:"/chapter/pdf-preview/73821",authors:[{id:"321704",title:"Dr.",name:"Pradip Kumar",surname:"Sarkar",slug:"pradip-kumar-sarkar",fullName:"Pradip Kumar Sarkar"}],corrections:null},{id:"73624",title:"BIM Approach for Smart Infrastructure Design and Maintenance Operations",doi:"10.5772/intechopen.94242",slug:"bim-approach-for-smart-infrastructure-design-and-maintenance-operations",totalDownloads:562,totalCrossrefCites:4,totalDimensionsCites:6,hasAltmetrics:0,abstract:"In the age of the Internet-of-Things and Big Data, Building Information Modeling (BIM) is being expanded into sectors for which it was not originally designed, such as the infrastructure sector, and becomes a necessity for the planning and management of smart cities. 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Cerebellar ataxias constitute a very heterogeneous group of diseases in which the motor incoordination is caused by the dysfunction and degeneration of the cerebellar neurons. Although different causative genes or toxins have been identified and several pathological pathways have been investigated, the treatments for these conditions are still largely palliative. Therefore, it is an urgent need for disease-relevant cellular models for studying disease progression and screening for potential therapies.
The rapid development in the field of induced pluripotent stem cell (iPSC) technology offers the opportunity to combine the genetic authenticity of the patient-derived cellular models with the disease-relevant cell types. Human iPSCs have been generated from a wide variety of easily accessible tissues, including skin and blood cells, using methods which nowadays are safer because they avoid the genomic integration of the viral vectors containing reprogramming factors. The potential of iPSCs to differentiate into any cell type of the body was previously explored by the studies with mouse and human embryonic stem cells (ESCs), which are blastocyst-derived pluripotent populations. Both iPSCs and ESCs may offer direct access to study the cells making the nervous system, but straightforth for disease models are the neurons differentiated from iPSCs, generated from patients with a variety of neurologic or neurodegenerative conditions [1, 2].
Although significant advances have been made, most of the protocols for the differentiation of human PSCs into neurons yield cellular populations which can only partially mirror the functional characteristics detected
As it happened for the generation of other human neural or non-neural cells and especially for the generation of the cerebral cells (reviewed in [3, 4]), the improvements in the generation of cerebellar neurons will definitely come from a better knowledge of the human cerebellum and its developmental pathways.
The human adult cerebellum is the second largest brain part (after the cerebral cortex) and contains around 80 billion neurons (which represents four times more neurons than in the cerebral cortex) [5, 6, 7, 8]. These neurons contribute to the complex cerebellar functions, including the control of movements for performing fine-tuning and coordination [9, 10], as well as of cognitive and emotional processes [11, 12]. The morphological and functional organization in the cerebellum, intensively investigated in rodents, is highly conserved across vertebrates [13]. Both human and mouse cerebella contain two lateral hemispheres connected by a region named vermis. The lateral hemispheres are subdivided into lobes and lobules and, together with vermis, covered by a uniformly pliated gray matter forming the cerebellar cortex. Cerebellar neurons have their cell bodies (somas) located in the cerebellar cortex and in the nuclei situated inside the white matter of each cerebellar hemisphere, called deep cerebellar nuclei (DCN). There are four distinctive DCN in mouse (dentate, fastigial, emboliform and globose), while the last two are fused as the interposed nucleus in human [10, 13].
The higher number of lobules in humans makes the cerebellar cortex more expanded relative to mice; in spite of the increase in size, both the volume of the cerebellum as a percentage of the total brain and the ratio of the number of neurons in the cerebellum to the cerebral cortex is remarkably constant across mammalian species, pointing to the concomitant increase of the cerebellum and the cerebral cortex in humans [6, 8, 14, 15, 16, 17].
The morphological organization of the adult cerebellum is schematically presented in Figure 1. The neurons located in the cerebellar cortex form three laminar structures laying between the internal white matter and the external pia mater: the granular layer (GL, named also the inner GL), the Purkinje layer (PL) and the molecular layer (ML). The GL contains the densely packed granule cells, which are the most abundant cell type in cerebellum and in the whole brain, as well as few other cells, such as Golgi cells (with different subtypes, such as Lugano, globular and candelabrum) and unipolar brush cells. PL is a narrow middle zone that contains the large cell bodies of the Purkinje cells, together with the cell bodies of a special type of glial cells named Bergmann glia. The ML contains mainly cell projections, but also a few entire neurons such as the basket cells located near the PL and stellate cells located near the pia mater.
Cellular composition and organization in the adult cerebellum. The cerebellum contains, from exterior to interior, the cerebellar cortex with 3 layers, the molecular layer (ML), the Purkinje layer (PL) and the granular layer (GL), and the deep cerebellar nuclei (DCN) situated in the white matter (WM). Excitatory (red-orange) and inhibitory (green) neurons are located in the cortex (granule cells (GC), unipolar brush cells (UBC), Purkinje cells (PC), Golgi (G), basket (B) and stellate (S) cells) and in the DCN (E-DCN and I-DCN). GC and UBC receive external afferents
In addition to the shape and location of their cell bodies, the cerebellar neurons are characterized by other intrinsic properties included in their neurochemical profiles (neurotransmitters, associated neuropeptides and receptors), electrophysiological profiles and, in the recent years, in high-throughput transcriptional fingerprints. Based on the neurotransmitters used for synaptic communication, cerebellar neurons are set into two
Regarding tissue architecture and connectivity, the cerebellar neurons are arranged as repeating units in a highly regular manner, relatively identical in all areas of the cerebellar cortex. Granule cells and excitatory neurons in DCN are projection neurons, while inhibitory neurons in the cortex (Golgi cells, stellate cells and basket cells) and DCN, and the unipolar brush cells are interneurons. Granule cells receive excitatory signals from neurons of the brainstem or spinal cord, mainly with a station in the middle or inferior cerebellar peduncle,
The axons of granule cells project to the ML, where they form the parallel fibers, which intercept the dendrites of Purkinje cells at right angles. There are ~200 granule cells per Purkinje cell in mice, while in humans there are 3000 granule cells
Remarkably, Purkinje cells can exhibit two distinct types of action potential, with simple and complex spikes. The simple spikes represent an autonomous pacemaker activity, with very little variability between spiking intervals, firing in absence of synaptic inputs. The simple spikes can be modulated by inputs from mossy fiber
A more extensive neuronal characterization was recently performed by high throughput sequencing, including single-cell sequencing for mouse and human cerebellar tissue [25, 26]. In spite of their quite regular morphology, the cerebellar neurons in each subclass appear as a heterogeneous population, different subsets being defined by several molecular cues, including co-neurotransmitters (e.g. glycine) and neuromodulators (e.g. calbindin, parvalbumin). Markers of some subclasses are related to the position in the cerebellar areas (reviewed in [27]). In addition, a comparative high throughput analysis of mouse versus human cerebellar cells using single cell-RNA sequencing showed that several genes are expressed in human but not in mouse Purkinje cells and confirmed at protein level the expression of novel and specific human Purkinje cell markers, in line with the data from the cerebral cortex [28, 29].
Recent progress in genetic technologies has significantly clarified how the cerebellar cells and their circuits are formed in model organisms, especially in mouse [30, 31, 32, 33]. Remarkable advances were made not only in defining of the molecular phenotypes and the differentiation pathways for most of the neural progenitors, but also in understanding of how these synchronize for forming neuronal circuits. Purkinje cells have major roles also during development [34]. They orchestrate the long lasting neurogenesis of the granule cells, the most abundant local excitatory neurons, and the maturation of the local inhibitory neurons, which reciprocally respond by helping in their own maturation.
The human-specific morphological and functional attributes were intensively studied over the last two decades, including for the development of the cerebellum. Mouse mutants for different genes related to developmental diseases affecting the cerebellum in humans demonstrated a considerable evolutionary conservation of the molecular programs across species, but also revealed some human-specific differences. Recent investigations of the developing human cerebellum have emphasized some differences in the organization of the cerebellar progenitor pools. Other human specific differences have been outlined by the single-cell sequencing of different brain cells, including cells in the cerebellum. These high throughput results point out that we still have much to learn about the human cerebellar development, composition and functions.
To what extent can or could the cellular diversity in the adult human cerebellum, and, in the same time, the spatial precision in its organization
The reported strategies for the differentiation of human PSCs toward cerebellar neurons, especially toward Purkinje cells, are reviewed in this chaper, followed by an outlook of their further optimization and diversification by implementing the knowledge from cerebellar development and new cell culture approaches. This outlook incudes an overview of the recent progress made in defining the cell populations in developing mouse and human cerebellum, followed by our current knowledge about their development, which includes pattering, proliferation, neurogenesis, gliogenesis, migration, connectivity and maturation. This knowledge is also the basis for the establishment and optimization of the PSC-derived models for cerebellar ataxias. An overview of the reported
Over the past 20 years, human PSCs, including the ESCs and the iPSCs [35, 36, 37, 38], have revolutionized the research on human development and diseases, particularly for the nervous system. Considerable progress has been made in converting human PSC into different types of neural progenitors, from which some continued to differentiate toward different classes of neurons,
Most of the reported human PSC-based protocols are an adaptation of the protocols that were previously developed for mouse ESCs, which reflect, to a various extent, different stages of neural differentiation in mouse embryo. On this line, the differentiation of the human PSCs is expected to reflect different stages of neural differentiation in human embryonic and fetal stages. Remarkably, recent data have demonstrated that several protocols starting from human PSCs produced authentic neurons and structured brain-like tissues, including the cerebral cortex, the most complex structure in the human brain. However, many questions remain about the extent to which the relative simplistic
For the neurons making the human cerebellum, the progress of
Increasing understanding of cerebellar development has allowed the elaboration of several protocols in the last years, which made the production of some classes of cerebellar neurons possible, with increasing efficiencies. These protocols were implemented in 2D and 3D cell cultures, or in their combination. As for other brain regions, the differentiation protocols include “directed” steps, meaning controlled differentiation by using extrinsic manipulation approaches, but also steps in which the differentiation advances spontaneously. Most of the protocols use morphogens/growth factors or small molecules with similar functions, which are sequentially administered to mimic the environment
Two early studies implemented the mouse ESCs differentiation into cerebellar neurons, using different approaches [44, 45], which were followed by several protocols aimed to increase their efficiency. Su et al. [45] used non-adherent ESC cell clusters in serum-free medium supplemented with fibroblast growth factor 2 (FGF2) and insulin. The cellular spheroids, named serum-free embryoid bodies (SFEB, even though they contained mainly undifferentiated cells in this stage), gradually differentiated into more complex 3D cell aggregates containing a mixture of progenitor cells and neurons, which included some granule cell progenitors and few neurons expressing early Purkinje cell markers. Following the same conditions, Muguruma et al. [46] showed that the FGF2-treated neural progenitors presented a broad fate, but some cells organized in tissue-like structures resembling the cerebellum origin in the embryo. These 3D cell aggregates further formed brain organoids, which contained some areas organized as a primitive cerebellar tissue. When cyclopamine, a sonic hedgehog (SHH) antagonist, was added to block the spontaneous ventralization, the proportion of cerebellar cells was increased, including 35–42% Purkinje cell progenitors by day 11 of ESC differentiation. Additionally, this study introduced the selection of the cerebellar progenitor cells, addressing to a cell-surface marker expressed in this population (Kirrel2/Neph3). The selected cells survived and integrated into the mouse cerebellum following
Salero and Hatten [44] succeeded in generating mouse ESC-derived granule cells at a relatively high efficiency by implementing a protocol in 2D culture based on step-related treatments with different morphogens. FGF8, WNT1 and retinoic acid (RA) were used in the first step, while bone morphogenic proteins (BMPs) were used in the next step to obtain the granule cell progenitors, which were next proliferated with SHH and Jagged1 and showed markers expressed in GL
The pioneering studies of mouse ESC cerebellar differentiation were next translated to human PSCs and subsequently refined (Table 1). The protocol of Muguruma et al. [46] in 3D culture was applied to human ESC and iPSCs [49, 50, 52]. Human progenitor cells self-organized in polarized neuroepithelium containing around 10% KIRELL2+ cells after 20 days. Muguruma et al. [50] also refined this protocol and followed a long-term ESC differentiation in 3D culture, an approach which resembled the first generation of human brain organoids. They found that the dorsal hindbrain patterning is more efficient for human cells without cyclopamine. Sequential addition of FGF19 and stromal cell-derived factor 1 (SDF1) generated approximately 28% KIRREL2+ cells (representing the progenitors of the cerebellar inhibitory neurons) and 18% ATOH1+ cells (representing the progenitors of the cerebellar excitatory neurons) by day 35. As for the mouse protocol, KIRREL2+ cells were subsequently selected by fluorescence activated cell sorting (FACS) and differentiated into Purkinje cells in co-culture with murine granule cell progenitors. The
General procedure | Hindbrain patterning | Cerebellar progenitors | Cell selection | Neuronal maturation | References | |
---|---|---|---|---|---|---|
VZ | RL | |||||
| FGF2, insulin, cyclopamin | KIRREL2+ | — | on organotypic cerebellar slices (rat, human) | Wang [49] | |
| KIRREL2+ | ATOH1+ | KIRREL2+ VZ progenitors by FACS | co-culture with postnatal mouse granule cell progenitors <150 days | Muguruma [50], Ishida [51] | |
| KIRREL2+ | ATOH1+ | — | co-culture with e18.5 mouse cerebellar progenitors <70 days | Watson [52] | |
| FGF8b and RA | KIRREL2+ | ATOH1+ | ATOH1-GFP+ by FACS | cell transplantation in mouse brain | Erceg [53] |
D0–4: WNT agonist (CHIR-99021) D4–12: FGF8b | KIRREL2+ | — | THY+ immature neurons by MACS | co-culture with mouse granule cells | Sundberg [54] | |
D0–4: WNT agonist (CHIR-99021) 1.5 μM D4–12: FGF8b (100 ng/ml) D12–24: BDNF | EN1/2 GBX2 (D6) | — | (D22) Negative selection for GD3 by immunopan-ning Positive selection for NCAM1+ immature neurons by MACS | co-culture with mouse cerebellar glia < 65 days and next with granule cells < 89 days | Buchholz [28] |
Reported protocols for the differentiation of human PSCs toward cerebellar neurons.
Other approaches aimed to increase the proportion of human ESC-derived cerebellar cells by applying the hindbrain patterning conditions tested for mouse ESCs [44]. Erceg et al. [53, 55] treated human ESCs aggregates with FGF8b and RA, followed by a manual selection of the neuroepithelial cells organized in polarized structures. This procedure yielded, after further differentiation, a heterogeneous population expressing markers of granule cells, Purkinje cells and glial cells. In a more directed differentiation approach, Sundberg et al. [54] used the WNT agonist CHIR99021, FGF8b and FGF2 for pattering the neuroepithelial cells resulted from the parallel neural induction of human ESCs with dual-SMAD inhibition [56]. The patterned progenitors gradually express the hindbrain, cerebellar and Purkinje cell progenitor markers, such as EN1/2, GBX2, PTF1a, KIRREL2 and SKOR2. Between days 24 and 48 of differentiation, markers of GABAergic phenotype and markers of immature Purkinje cells, such as PCP2, were detected. In order to enrich for the Purkinje cell population, instead of the previously used cell sorting for KIRELL2, Sundberg et al. [54] implemented the THY1+ cell selection, a method previously used to purify mouse Purkinje cells from primary cerebellar cultures [57]. The sorted THY1+ cells further matured into Purkinje cells expressing the early Purkinje cells marker PCP2 (or L7). The same team further optimized the directed differentiation protocol [28], by quantifying the effect of patterning molecules on directing the cerebellar cell phenotypes. They found that the combination of the GSK3 inhibitor CHIR99021 (1.5 μM) for 4 days with FGF8b (100 ng/ml) between days 5 and 12 of differentiation generated the highest proportion of Purkinje cell progenitors. From days 12 to 24, neural cell expressing the cerebellar marker KIRREL2 gave rise to increasing numbers of adjacently located cells expressing Purkinje cell markers. As early as day 35 of differentiation, subpopulations of iPSC-derived cells expressed markers of the primary cerebellar progenitor cells. The postmitotic Purkinje cell marker PCP2 was observed starting from day 18 onward. Flow cytometry analysis showed that ∼23% of cells expressed PCP2 at day 24 of differentiation. A changing element of this protocol was the selection of the immature human PSC-derived Purkinje cells in two steps, a negative selection by GD3 immunopanning and a positive selection by magnetic cell sorting (MACS) with NCAM antibodies [28].
As for the mouse cerebellar neurons, the conditions used for the
Even though the reported protocols have advanced in the generation of cerebellar neuron from human PSCs, they still need a lot of optimization in order to generate homogeneous population of cerebellar neurons in 2D cultures or cerebellar tissue-like aggregated in 3D cultures. Looking at the previous optimizated protocols for generating other neuronal populations, such as the midbrain neurons, the cortical neurons or the cortical organoids, it is relevant to follow again the steps which were gradually applied in order to achieve the efficiency and complexity they offer today (reviewed in [3, 4]). Following this aim, here the development principles of the cerebellar neurons are overviewed, from progenitor specification to neuronal assembles, followed by an outlook of how these principles could be applied for the optimization of the protocols generating cerebellar neurons from human PSCs.
During early embryo development, the human neural tube is formed by the folding of a sheet of neuroepithelium and is progressively closed and regionalized under the control of temporally and spatially coordinated gradients of morphogens secreted by organizer centers. At the end of the neurula stage, corresponding to embryonic day (E) 28, the neural tube is entirely closed and contains, from anterior to posterior, the three primary brain vesicles (forebrain, midbrain and hindbrain) and the spinal cord. Soon after the definition of the midbrain-hindbrain boundary (MHB), cerebellum starts to form at the most anterior and dorsal hindbrain territory. In humans, the cerebellar development is highly protracted, extending from E30 to the end of the second postnatal year. In mice, cerebellum almost completes over a period of around one month, starting from embryonic day (e) 9 and including the first three postnatal weeks (reviewed in [15, 58, 59, 60] (Figure 2). However, as for the whole brain, the mechanisms of cell differentiation and histogenesis in cerebellum are mainly conserved in mammals. While the development of the mouse cerebellum was intensively studied [15, 30, 32, 33, 34, 58, 61, 62, 63, 64, 65], the embryonic and fetal stages in human cerebellar development were only recently described in details [13, 16, 59, 60]. Notably, as for the other parts of the human brain, the embryonic and fetal stages of development are not available for cellular and functional studies, and their histological and clinical images represent only snapshots in time for one individual. Conversely, developmental time-course experiments in mice can be conducted on multiple mice of identical genotypes. These studies revealed that the ontogenesis of all neurons and glial cells in the nervous system, including the ones in the cerebellum, follows the same steps of (1) patterning and specification of the progenitor cells, (2) neurogenesis/gliogenesis and (3) migration, histogenesis, formation of the neuronal circuits and neuronal maturation (reviewed in [15, 27, 58, 61, 66, 67]). However, in contrast to other CNS areas, including the cerebral cortex, in which gliogenesis follows neurogenesis [68, 69], glia generation in cerebellum parallels or precedes the long-lasting generation of the granule cells and inhibitory neurons [15, 30, 32, 65, 68]. Even though the main developmental programs are conserved from mice to humans, some important specie-specific differences responsible for the expansion of the human cerebellum have been recently identified [59, 60]. In the following brief presentation, the main morphological, cellular and molecular events in mouse are complemented with the available information in human.
Timing and the aligned stages in mouse and human cerebellar development. Embryonic days in mouse (e) and human (E). GW-gestational weeks. NE- neuroepithelium (light blue). The cerebellar ventricular zone (VZ) (blue) is the origin of the inhibitory neurons and glial cells. Inhibitory neurons (green) are the Purkinje cells (PC), Golgi, basket and stellate cells, and the inhibitory neurons in the deep cerebellar nuclei (I-DCN). The rhombic lip (RL) (Lila) is the origin of the excitatory neurons in the cortex (Granule cells (GC) and unipolar brush cells (UBC) and in the DCN (E-DCN). Long-lasting progenitor stages for the GC progenitors (GCP-violet), and inhibitory interneuron and glial progenitors (INP and GP, blue). Long-lasting maturation of inhibitory neurons (light green) and of excitatory neurons (light pink-orange), and gliogenesis (gray) stages.
Several studies in mouse showed that all cerebellar neurons and glial cells originate from the hindbrain region corresponding to the dorsal (or alar) part (or plate) of the first rhombomere (r1) [30, 70]. The anterior limit of the cerebellum is defined by the MHB, named also isthmus, where an organizer center, named the isthmus organizer (IsO), forms early in development and has a major role in the anterior/posterior (A/P) patterning of the midbrain and hindbrain. IsO formation is preceded by a series of pattering events that start in the forming neural plate, where two transcription factors, Otx2 (Orthodenticle Homeobox 2) and Gbx2 (Gastrulation Brain Homeobox 2) define the primitive anterior and posterior domains, respectively [71]. They are further co-expressed in early IsO and then differentially express in the midbrain and hindbrain domains [72]. WNT signaling has a main role in the A/P patterning of the neural tube but also in IsO induction, showed by the loss of IsO in WNT1 homozygous mutants ([73]; reviewed in [74]). Shortly after the primary brain vesicles formation, Fibroblast Growth Factor 8 (FGF8) secreted by IsO patterns the adjacent territories [71, 75, 76, 77, 78, 79, 80]. Additional A/P patterning by extra-neurally secreted retinoic acid (RA) defines the metencephalic and myelencephalic secondary hindbrain vesicles. The metencephalon expresses the homeobox gene
Stages and distribution of cell populations in mouse early cerebellar development. Formation and differentiation of the cerebellar populations from embryonic day (e) 8 to e16, when all the neuronal populations or their long-lasting progenitors are formed. (A) Between e8 and 12, in the dorsal part of the first rhombomere (r1) of the hindbrain neural tube, the cerebellar ventricular zone (CVZ) (light blue) forms at e9–10, due to the dorsal FGF8 signal and ventral SHH signal, while the rhombic lip (RL) forms at after e10, being visible at the border between the CVZ and the roof plate (RP) (light Lila), due to the BMP signaling from the RP, which forms the choroid plexus epithelium (ChPe) (red). (B) Between e12 and 16, different progenitors arrive in the subventricular zone (SVZ) and mantle zone (MZ) of the neural tube. At e12–14, the Ptf1+ ventricular zone (VZ) of the CVZ primary domain contains the Olig2+ and the Gbx1+ subdomains, which generate the Purkinje cell progenitors (PCP) and the interneuron progenitors (INP, blue) domains, respectively, while the first postmitotic Purkinje cells (PC) already exit the SVZ. The VZ in RL contains Atoh1+ progenitors, which gradually form progenitors of the excitatory neurons in SVZ. They generate first the excitatory neurons for the deep cerebellar nuclei (E-DCN) and at later time points (e14–16), they start to generate the unipolar brush cells (UPC). The RL generates also the progenitors of the granule cells progenitors (GCP-violet), which migrate in waves in the MZ close to the pia mater (PM). In the CVZ, cells representing a subpopulation of the INP domain migrates in the MZ and join the E-DCN in a nuclear transitory zone (NTZ), where they start to differentiate into the inhibitory neurons of the DCN (I-DCN).
Between e9 and e12.5 and, the cerebellar neuroepithelium undergoes morphological changes: the midline remains as a single cell layer and forms the roof plate, while each lateral part forms two primary proliferative zones, known as the origins of the neural populations in the mouse cerebellum: the cerebellar ventricular zone (VZ) and rhombic lip (RL) (Figures 2 and 3) [30]. By e10, the roof plate becomes the second cerebellar organizer center and secretes factors belonging (TGF)-β family, such as the bone morphogenetic proteins (BMPs), the most important dorsalizing factors in the cerebellum, and gradually transforms into the choroid plexus epithelium (ChPe). By e12.5, ChPe additionally produces SHH. Genetic fate mapping proved that the morphogens secreted by IsO, roof plate and floor plate define the cerebellar domains which, in addition to the hindbrain restricted expression of Gbx2, show the differential expression of two basic-helix–loop–helix (bHLH) transcription factors: Pancreatic transcription factor 1 (Ptf1) specifies the VZ domain and Atonal homolog 1 (Atoh1, also called Math1), specifies the RL progenitor domain [15, 58, 61, 84, 85].
Each cerebellar progenitor zone forms subdomains with their own spatial and temporal identities, which produce specific neuronal subtypes. VZ-derived progenitors give rise to all GABAergic neurons and glial cells of the cerebellum. VZ-derived neurogenesis starts at e10.5 and continues untill e17 in mouse. Before the neurogenesis starts (~e9), the VZ progenitor domain corresponds to the neuroepithelial cells localized in the VZ of the r1 neural tube (Figure 3). Most of the earliest Ptf1a + progenitors upregulate
The neuroepithelium of the RL gives rise to all glutamatergic neurons in the cerebellum (Figures 2 and 3), but also to extracerebellar neurons such as the pontine neurons [66, 70]. RL Atoh1+ neuroepithelial cells situated between the roof plate and the VZ domain start their proliferation after the adjacent VZ progenitors (~e10). Also the RL neuroepithelial cells gradually acquire a radial glial phenotype and are patterned in subdomains, which express the paired box gene
The cerebellar proliferative zones in human embryos have been only recently investigated. The human cerebellar VZ (gradually forming the SVZvz) undergoes massive expansion which covers the second month (E30–56), afterwards extinguishing its proliferative potential and remaining as a single cell layer. Conversely, the RL germinal zone remains small during the peak expansion of the VZ progenitors, but starts a significant expansion at around gestational week (GW) 11, when it forms the SVZRL, which persists long after birth [59, 60].
In humans, all Purkinje cells are generated before the 8th GW, which places them among the earliest-born central neurons. They start to migrate at E44 outwards from the VZ along radial glial projections to the pial surface. A broad Purkinje cell multilayer extending in the mantle zone is evident between the GW 10 and 13 GW, while a monolayer distribution is achieved by GW 20–24 (Figure 2). Human Purkinje cells start to develop their characteristic extensive and flattened dendritic arbors and long axons in the early fetal stages, their final maturation being achieved postnatally, in a 6-fold longer period than in mice [59, 60, 90, 91].
Contrary to the Purkinje cells, which are postmitotic already into the cerebellar SVZvz, the Gbx1+ progenitors expressing the paired homeobox gene
Glutamatergic cerebellar neurons (excitatory neurons in DCN, granule cells and unipolar brush cells) originate from different subdomains of the RL, in different waves (Figures 2 and 3). The first cells leaving from the RL are the newborn excitatory neurons in DCN. Next, the granule cell progenitors migrate in waves out of the RL, where they continue the proliferation. In the first wave (e10.5–12.5), discrete subpopulations of rostrally situated Atoh1+ cells gradually upregulate
The second wave covers middle to late embryonic stages, when Pax6+ granule cell progenitors leave the RL, migrate out toward the pial surface and undergo a prolonged expansion in a secondary germinal zone, or a second transit amplifying center, named the external granular layer (EGL) [64]. Granule cell progenitors retain the expression of
Unipolar brush cell differentiation parallels the granule cell progenitor waves (Figure 2). Unipolar brush cells are born starting with e13.4, while continuing to p0–1. Progenitors of the unipolar brush cells express Wnt1 early in development (e10.5–13.5), but this expression is downregulated before they migrate from the RL. The newly generated neurons remain in the RL for an additional 1–2 days, after which they exit RL and migrate dorsally through the white matter to their final destination. Most unipolar brush cells reach the IGL by p10, several days before granule cell neurogenesis is complete. Their final maturation occurs between p2 and p28, which seems to coincide with the establishment of the first synaptic contacts with external mossy fibers [15, 27, 88].
The successful construction of the neuronal circuitry relies on the coordinated generation of functionally opposed neurons. Accordingly, the differentiation programs of cerebellar excitatory and inhibitory neurons are interdependent and defined as the coordinated integration of the VZ and RL-derived lineages in local circuits, in both the cortex and DCN. For the DCN, the cell fate of the excitatory neurons appears determined at the RL, in a temporal pattern, while the interneuron progenitors migrate, differentiate and integrate in the NTZ after receiving local signals from the excitatory neurons.
Purkinje cells have a remarkable capacity to regulate developmental events by sending SHH signals bi-directionally. Starting at e16.5 and continuing throughout adulthood,
In the third trimester and postnatally, human cerebellum undergoes its major growth, primarily due to the prolonged expansion of the granule cell progenitors. By 10–11 GW, streams of cells which form the external GL (EGL) were observed along the pial surface connecting to the RL. Due to extensive EGL proliferation, human cerebellum increases 5 fold in size between GW 24–40 [90]. Differentiation and maturation of the human cerebellar neurons progress mainly as in the mouse, but there are some species-specific features. Foliation correlates with EGL proliferation and increases dramatically between GW 20–32, as the cerebellum rapidly increases in size and volume. The formation of the Purkinje cell monolayer coincides with the peak of EGL proliferation [89, 90]. The human cerebellar cortex still has a prominent EGL at birth. EGL gradually decreases in thickness as a result of migration of granule cells into the internal GL. By the end of the second postnatal year, EGL is depleted while the thickness of the molecular layer and the length of the PL increase, concomitant with the increasing cerebellar volume [89, 90]. To date, there are few studies about the development of the human interneurons, both inhibitory and excitatory, which represent a minority comparing to the granule cells, but with a major role in the maturation of Purkinje cells and circuit formation [15, 34, 58, 91, 101].
In addition, the single-cell sequencing techniques have been applied for analyzing different stages of mouse cerebellar development [62, 102]. Carter et al. [62] performed single-cell RNA-sequencing and unbiased classification of around 40 thousand murine cerebellar cells from eight embryonic samples (at e10-e17) and 4 postnatal samples (at p0, p4, p7 and p10). Such approach allows for a more comprehensive detailing of the transcriptional and cellular heterogeneity among lineages of interest and can provide a valuable resource for answering further questions related to cerebellar development and diseases. In a similar study, Peng et al. [102] analyzed around 20 thousand cells from mouse postnatal cerebella and looked in addition to the dynamics of interneuron differentiation but also mitochondrial markers and ataxia risk genes. In a complementary approach, gene expression in the postnatal stages of mouse cerebellar development were analyzed by Buchholtz et al. [28] in Purkinje cell populations selected from mice expressing a
There are several steps to be considered for the cerebellar protocols, which practically cover all the developmental stages: from neural induction and dorsal hindbrain patterning to the patterning and proliferation of the VZ-like and RL-like progenitors, to the neurogenesis of the selected progenitors, and lastly to the maturation of the neurons and the formation of the neuronal circuits. Are the previously used neural induction and early patterning conditions (in both 2D and 3D approaches) optimal for the generation of progenitors similar to the ones in the dorsal r1 in the neurula stage, which represent the origin of the neurons making the cerebellum? Are the previously used conditions optimal for the uniform generation of early VZ and RL progenitors? Which factors and what timing would be necessary for a uniform patterning towards VZ or RL subpopulations? Which conditions would be efficient to produce a uniform neurogenesis from different progenitors? What would the defined conditions for the neuronal maturation be? How can the neuronal maturation be faster? How can other neuronal subtypes, such as the interneurons in the cerebellar cortex and in the DCN, be generated uniformly and efficiently?
Some recent strategies were successful for the optimization of the protocols for the cerebral neurons and cerebral organoids. It remains to be checked whether these strategies can be extrapolated for the cerebellar cultures. Again, the solutions may come from the development principles. The main traiectories that could be followed from the human iPSC to the neuronal cell types contained in the cerebellum are outlooked in Figure 4 and detailed in the following paragraphs.
Some previous protocols used FGF2 for amplifying the neuroepithelial population and showed that, although an anterior phenotype is kept for a few passages in the presence of FGF2, longer exposure gradually patterns human progenitors toward midbrain and hindbrain fates [105, 107, 108]. FGF2 was used by Muguruma et al. [50] for inducing a brought midbrain-hindbrain patterning, including the IsO-like cells, in 3D spontaneously differentiating human PSCs in serum-free medium, for a time approximating the MHB formation in human embryos. However, the reproducibility of this protocol is limited and the efficiency of the neural induction and pattering was not investigated, many cells in the 3D clusters could present a more anterior phenotype (and maybe non-neural phenotypes). Watson et al. [52] proposed the parallel neural induction and hindbrain patterning by using FGF2 in combination with the SMAD inhibitor SB431542 for around 20 days. Even though it showed an increased expression in hindbrain and cerebellar markers, yet the efficiency and the selectivity of this approach was not reported.
The implementation of WNT signaling was shown to increase the midbrain and hindbrain patterning and reduce the spontaneous forebrain patterning in human PSC-derived neural cultures [28, 41, 54, 109, 110]. In Kirkeby et al. [41] and Kirkeby et al. [110], neural induction with dual-SMAD inhibition and pattering were applied in parallel for 9 days. The GSK3 inhibitor CHIR99021 was used at 1–2 μM concentration for patterning the anterior r1 fate. Following this protocol with some modifications, Sundberg et al. [54] applied the neural induction and hindbrain patterning by WNT in the same time, for 12 days, with noggin and 1.7 μM CHIR99021, while in a following study coming from the same group [28], neural induction and patterning with CHIR99021 1.5 μM was applied for only 4 days. In both studies, FGF8b (100 ng/ml) was added from day 4 to day 12 of differentiation, while FGF2 applied at day 10–12 in Sundberg et al. [54] was excluded in the next protocol [28]. However, the resulted cell populations in both studies were not directly phenotyped, but after 16 or 32 days of differentiation, when they contained KIRREL2+ or THY1+ cells, respectively, which were selected by FACS. Further optimization for neural induction and hindbrain patterning requires a deeper investigation, including negative markers for forebrain, midbrain, hindbrain (excepting the r1), and ventral markers (especially for the r1). The dorsal r1 cells should concomitantly and uniformly express GBX2 and EN1/2. Obviously, reporter lines for different genes expressed solely in r1, such as HOXA1, would be very useful tools.
In addition, a study using human hindbrain tissue from embryos at GW 5–7 showed that the hindbrain neuroepithelial cells were stably expandable in FGF2 and EGF conditions, but the short treatment with FGF8 and WNT (for 1 passage) hugely increased the expression of GBX2, EN1 and EN2 [111]. A deeper investigation of the human embryonic dorsal hindbrain tissue could provide hints for the optimization of the human PSC differentiation protocol toward cerebellar cells. The human embryonic hindbrain neuroepithelial cells can be further patterned
Another approach can come for the optimization of long-term cultures of cerebellar organoid, in line with the extensively investigated field of cerebral organoids [39]. As shown in different previous reports, functional synaptic connections are necessary for maturation and activity of the human PSC-derived neurons, which include glia and target neurons, all of these could be provided in the same cerebellar organoid.
Again, one limitation for most of the human PSC-derived neurons, as for the human neurons in general, is the lack of transcriptomic signatures, to rigorously identify specific types of neurons and to compare their development across species. A recent Metagene projection analysis of global gene expression patterns revealed that differentiating human PSC-derived Purkinje cells share classical and developmental gene expression signatures with developing mouse Purkinje cells. Remarkably, it revealed that the human PSC-derived Purkinje cells matured in co-culture for around two months are closest to late juvenile (p21) mouse Purkinje cells, suggesting that they are relatively mature. Gene expression profiling also identified human-specific genes in human PSC-derived Purkinje cells. Protein expression for one of these human-specific genes CD40LG, a tumor necrosis factor superfamily member, was confirmed in native human cerebellar tissue, arguing for the bona-fide nature of the human PSC-derived cerebellar neurons [28]. Obviously, the routine applications of the single-cell transcriptomics into the optimization steps of the human PSC-derived cerebellar differentiation protocols will hugely contribute to the progress in the field.
The iPSC technology together with the cerebellar differentiation protocols offer the opportunity to indirectly generate and to directly study the most affected cells in patients with cerebellar ataxias, the cerebellar neurons. As schematically presented in Figure 5, somatic cells such as skin fibroblasts or white blood cells obtained from patients are reprogrammed into iPSCs, which can be theoretically differentiated into any type of neurons. Ideally, the neuronal differentiation should address the most affected subpopulation in each disease, by following the existing protocols or optimized protocols in the desired direction (using development principles and combining efficient selection methods). Remarkably, for the inherited ataxias, the patient iPSC-derived neurons express the disease mutation in the authentic genetic background and cellular environment, which is not the case in the animal models.
From ataxia patients to neuronal disease models. Somatic cells from patients with cerebellar ataxias are reprogrammed into induced pluripotent stem (iPS) cells, which can be genetically modified in order to correct the mutation. Patient and control/corrected iPS cells can be differentiated into neurons that are relevant for the cerebellar diseases, such as Purkinje cells. Additional stress or forced aging can be equally applied to the patient and control/corrected neurons or their progenitors, in order to amplify the phenotypic differences resulted from the ataxia’s specific mutation.
The neuropathological events in hereditary cerebellar ataxias affect both cerebellar and extracerebellar territories. Nevertheless, degeneration and ultimate loss of cerebellar neurons is a neuropathological hallmark in cerebellar ataxias. The affected cerebellar neurons and the responsible genes for several cerebellar ataxias are presented in Table 2. Spinocerebellar ataxias (SCAs) are a family of over 40 currently described late-onset dominant diseases, manifesting clinically at middle age and gradually progressing with neurodegeneration in cerebellum and other CNS areas, [136, 137, 138, 139] while in other genetic ataxias, such as the autosomal recessive Friedreich ataxia (FRDA) and ataxia-telangiectasia (AT), the disease manifests a lot earlier and, in addition to the nervous system, extraneural territories are affected [137, 138]. FRDA is considered a multi-systemic condition, including central and peripheral neuropathies, diabetes and cardiomyopathy [140, 141].
Ataxia Type | Affected cerebellar neurons | Gene, mutation&location | Affected protein | Human iPSC-derived neurons | Human iPSC-derived models References |
---|---|---|---|---|---|
PCs++, DCN++ | Ataxin-1 | — | [114, 115] | ||
PCs+++, DCN+++ | Ataxin-2 | CNS | [116, 117] | ||
PCs+, DCN+++ | Ataxin-3 | CNS | [117, 118, 119, 120, 121, 122] | ||
PCs+++, GCs+, DCN++ | α1A & α1ACT | cerebellar | [51, 107] | ||
PCs+++, DCN+++ | Ataxin-7 | CNS | [123, 124] | ||
PCs+++, DCN+++ | PP2R2B | — | [125] | ||
PCs+++, DCN+++ | TBP | — | — | ||
— | — | — | |||
PCs+++, DCN+++ | — | CNS | [126] | ||
PCs+++, DCN+++ | Cav3.1 | cerebellar | [127] | ||
PCs+, DCN+++ | Frataxin | PNS, CNS | [128, 129, 130, 131, 132, 133, 134] | ||
PCs+++, GCs+++ | ATM | cerebellar | [135] |
Affected cerebellar neurons and iPSC-derived models for different ataxias.
PCs-Purkinje cells, GCs-granule cells, DCN-deep cerebellar nuclei, +++high ++ medium, +low.
In cerebellum, SCA1, SCA3 and FRDA involve mainly the DCN, especially the dentate nucleus, but also extracerebellar territories such as the Clarke’s column, which present with severe neuronal loss (reviewed in [142]). SCA2 predominantly affects the pontine nuclei, while the Purkinje cells and DCN seem to be secondarily affected. SCA31 is relatively restricted to the Purkinje cells. Although Purkinje cells are predominantly involved in SCA6, degeneration is evident also in the dentate nucleus and granule cells. Therefore, patients with SCA6 show more severe ataxia than those with SCA31. Several SCA subtypes have CAG repeat expansions in the coding region of different genes (http://www.scabase.eu/; [143, 144, 145, 146]), resulting in PolyQ elongations in the respective proteins, the elongation size being correlated with the intensity of clinical manifestations. In other SCAs (SCA12, SCA31 and SCA36) or non-SCA monogenic ataxias, such as FRDA, the repeat expansion is intronic, but also in these diseases the cerebellar dysfunction is correlated with the elongation size [147].
Modeling these human genetic disorders in mice has reproduced to a certain extend the neuropathological aspects and has provided some insights into disease mechanisms. Many disease mechanisms that have been explored in mouse models are expected to be recapitulated in patient iPSC-derived neurons. However, some ataxias could not be modeled in mice using the same mutation as in the patients, suggestion that the human-specific environment is essential for the disease to develop. Additional mechanistic understanding of the network of events produced by the mutation is crucial for the development of effective therapies, as none of the cerebellar ataxias is yet curable, treatable or preventable [143, 145, 147, 148, 149].
For modeling cerebellar ataxias, the iPSC-based models present three main advantages. First, most of cerebellar ataxias are monogenic diseases. Second, neurons bearing the mutation, which are not directly available from patients, can be generated
However, as presented in Table 2, relatively few studies have succeeded in generating iPSC-based models for cerebellar ataxias. An additional important question for the iPSC-based models is to what extend the mutated gene is expressed in the neurons generated
A handful of studies published to date addressed iPSC models of PolyQ SCAs (such as SCA1, 2, 3, 6, 7 and 12), non-PolyQ SCAs (such as SCA36 and 42), and other ataxias (such as FRDA and A-T). Most of the iPSC-based models used a generic differentiation towards the neural lineage, as opposed to the generation of specific neuronal subtypes, and very few characterized the neuronal phenotypes. The only reported iPSC-derived models addressing the cerebellar neurons were for SCA6 [51], SCA42 [127] and A-T [135].
For SCA1 and SCA12, only the generation of patient-derived iPSCs were until now reported [114, 115, 119, 125]. Several other SCA models have already addressed the neural phenotypes. SCA2 was modeled by Xia et al. [116] and by Chuang et al. [117] using patient iPSC-derived neural progenitors and central neurons. No cerebellar protocol has yet addressed SCA2, in which both Purkinje cells PCs and DCN neurons are affected. Whereas patient and control fibroblasts showed comparable levels of expression of the disease-causing protein Ataxin-2, its expression was decreased in patient iPSC-derived neural stem cells, which survived shorter in cell culture. Chuang et al. [117] reported that SCA2 neurons exhibited a glutamate-dependent disease phenotype, which are suppressed by anti-glutamate drugs and a calcium stabilizer treatment.
One of the first studies using the generation of neurons from patient iPSCs addressed to SCA3, also called Machado-Joseph disease (MJD) [118]. In this model, neuronal excitation by glutamate promoted an increase in intracellular calcium concentration and proteolysis of Ataxin-3, triggering its aggregation—a hallmark of the disease in patients. This intraneuronal aggregation, (which was also found to depend on sodium and potassium channel function, as well as on ionotropic and voltage-gated calcium channel function), was abolished by calpain inhibition, pointing to a key role of this protease in Ataxin-3 cleavage. Furthermore, intracellular aggregations were not observed in patient iPSCs, fibroblasts or iPSC-derived glial cells, providing a clue for the neuron-specific phenotype observed in SCA3 patients. Hansen et al. [120] differentiated the SCA3 patient-derived iPSCs further into hindbrain neurons that expressed
SCA6 is a very interesting case, first, by being one of the three diseases in which patient iPSC-derived cerebellar neurons were generated to date, and second, because of the bicistronic nature of the affected gene,
For SCA7, in which cerebellar and retinal cells are degenerated [151], Luo et al. [123] reported the generation of iPSCs and neurons from a SCA7 patient, but did not characterize the neuronal phenotype and the disease phenotype. Ward et al. [124] generated SCA7 patient-derived iPSCs and their isogenic lines transduced with either normal or expanded ATXN7. They reported that SCA7 iPSC-derived neural progenitors exhibit altered metabolism and mitochondrial dysfunction.
SCA36 and SCA42 are non PolyQ autosomal dominant diseases, affecting the cerebellar neurons and other neurons. Matsuzono et al. [126] generated motor neurons from the patient-derived iPSCs and recapitulated an increase in RNA foci-positive cells that can be markedly suppressed by treatment of antisense oligonucleotide. SCA42 is caused by a mutation in
For the FRDA, a pioneering work revealed that abnormal expansion of GAA repeats led to upregulation of the DNA mismatch repair protein MSH2 in FRDA patient-derived iPSCs [130]. They reported that the functional inhibition of
For the A-T is caused by several mutations in the
Of particular interest in future research in the cerebellar ataxias is the comparison between affected and unaffected neuronal types, in order to identify particular characteristics that render specific neuronal populations vulnerable to a genetic insult which is ubiquitously presented. One of the most crucial needs is to establish a reliable and consistent disease phenotype in a relevant cell population, and those cell types to be generated in relatively large quantities
Differentiation into specific and mature neurons that are the disease targets, such as Purkinje cells for several SCAs, or solely DCN neurons for some ataxias, or both of them for the most of SCAs (Table 2), will enable the construction of more reliable disease models [154]. However, the suitability of iPSC-derived neurons for modeling late-onset conditions remains controversial, particularly given the immature, fetal-like phenotypes of the neurons generated from these cells.
Remarkably, in contrast to the immature morphology observed for human PSC-derived Purkinje cells, a recent bioinformatics analysis of their gene expression and developing showed that they most closely resembled late juvenile p21 mouse expression mouse Purkinje cells, when most of the cerebellar disease phenotypes in several animal models start to manifest. This finding suggests that the Purkinje cells are among the most mature human PSC-derived central neurons analyzed to date. This approach also underscores the utility of transcriptomic analysis for analyzing the maturation of human PSC-derived neurons and validates the use of hPSC-neurons for modeling cerebellar ataxias.
Still, it is possible that the disease phenotypes of adult-onset conditions, as the most of genetic SCAs are, may never be fully recapitulated under 2D cell culture conditions, even with directed protocols and optimized maturation. Generation of 3D cerebellar-like tissues as the cerebellar organoids may allow to increasing the neuronal maturation
Another way to model the late-onset diseases is the addition of neural stressors, such as reactive oxygen species, pro-inflammatory factors, and toxins or forced aging, as schematically presented in Figure 5. These approaches were already used for modeling several SCAs or other neurologic diseases [153, 155, 156, 157]. However, in an ideal situation, these stressors should only exacerbate the disease phenotype, which can be evident in a good model solely by the expression of the mutation in the disease-relevant cells. Another approach is to genetically manipulate the system for forcing the aging, such as by overexpression of progerin in neural progenitors. By this approach, the disease phenotype is expected to manifest
On the other side, recent evidence from cell and animal models indicates that abnormalities in early Purkinje cell development may contribute to the pathogenesis of the ataxias Purkinje cell developmental abnormalities are clearly evident in a wide range of ataxic mouse mutants, including models of the degenerative SCAs [26]. The observed Purkinje cell developmental defects commonly include impaired dendritic arborization, resulting in synaptic deficits affecting CF and PF connections and ultimately altering Purkinje cell physiology. Similar impairments in Purkinje cell dendritogenesis and synapse formation have been described in mouse models of SCA5, and in cell and mouse models of SCA14, SCA1, SCA3 and SCA5. Given the increasing evidence for Purkinje cell developmental abnormalities in cerebellar ataxias, it seems likely that iPSC-derived models, which are capable of recapitulating early developmental events
Another limitation in the field of modeling cerebellar ataxias is that most of the studies implemeted the production of iPSCs from a few patients. On one hand, addressing to larger patient cohorts may allow to identifying more accurate phenotypes. On the other hand, for investigating the pathological function of a mutation, the ideal situation is to compare the cells bearing the mutation with control cells with an identical genetic background. The rapid development of CRISPR/Cas9-mediated genome editing is likely to result in significant advances in the field, allowing the correction of disease-causing mutations into iPSCs, which can then be used to create paired isogenic lines to produce better disease models in which far less patient-derived cell lines will be necessary [160]. This was already performed even for the ‘difficult to correct’ elongations, like in SCA3, SCA7, it is expected in the near future to constitute ‘the norm’ for all iPSC-derived disease models.
The establishment of efficient, reproducible cellular models of cerebellar dysfunction and degeneration will be important not only in elucidating the molecular basis of these diseases, but also in the development of effective therapies. Establishment of special cell cultures, such as Purkinje cells from patients with cerebellar ataxia, provides opportunities to screen for drugs that may correct the observed disease phenotypes. These cell cultures can be combined with stressors capable of eliciting phenotypes in late-onset conditions and genotypic modifiers of disease progression and drug response. In addition, these cerebellar cell cultures may be used for toxicity screens, to assess the effects of novel compounds on relevant cell types, or for differentiation screens, to identify compounds capable of enhancing self-renewal, maturation or survival of specific cerebellar cells (Figure 5).
Recent technologies for producing iPSCs from patients combined with the differentiation of PSCs into neural cells and the self-organizing 3D neural tissues have provided a new way to experimentally investigate the developmental and disease mechanisms of the human brain. While several challenges have hindered the generation of cerebellar neurons
However, human PSC-based models offer distinct advantages for the study of cerebellar ataxias. Cerebellar neuronal models are likely to provide valuable insights into the selective vulnerability of distinct neuronal subtypes, particularly the Purkinje cells. More directed and/or complex approaches will allow for the generation of accurate, disease-relevant models for the study of the molecular mechanisms underlying cerebellar ataxias, and the development of the long-awaited therapies.
This work was supported by Austrian Science Fund (FWF), Project P26886-B19, Austria.
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From Old Problems to New Challenges"},signatures:"Andreas Schmeling, Pedro Manuel Garamendi, Jose Luis Prieto and María Irene Landa",authors:[{id:"34264",title:"Prof.",name:"Pedro Manuel",middleName:null,surname:"Garamendi Gonzalez",slug:"pedro-manuel-garamendi-gonzalez",fullName:"Pedro Manuel Garamendi Gonzalez"}]},{id:"19161",doi:"10.5772/19234",title:"Diagnostic of Drowning in Forensic Medicine",slug:"diagnostic-of-drowning-in-forensic-medicine",totalDownloads:8199,totalCrossrefCites:9,totalDimensionsCites:18,abstract:null,book:{id:"243",slug:"forensic-medicine-from-old-problems-to-new-challenges",title:"Forensic Medicine",fullTitle:"Forensic Medicine - From Old Problems to New Challenges"},signatures:"Audrey Farrugia and Bertrand Ludes",authors:[{id:"34146",title:"Dr.",name:"Audrey",middleName:null,surname:"Farrugia",slug:"audrey-farrugia",fullName:"Audrey Farrugia"},{id:"49284",title:"Dr.",name:"Bertrand",middleName:null,surname:"Ludes",slug:"bertrand-ludes",fullName:"Bertrand Ludes"}]},{id:"19172",doi:"10.5772/22792",title:"Advanced Medical Imaging and Reverse Engineering Technologies in Craniometric Study",slug:"advanced-medical-imaging-and-reverse-engineering-technologies-in-craniometric-study",totalDownloads:4538,totalCrossrefCites:2,totalDimensionsCites:6,abstract:null,book:{id:"243",slug:"forensic-medicine-from-old-problems-to-new-challenges",title:"Forensic Medicine",fullTitle:"Forensic Medicine - 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These include situation of death, anatomical and histological analysis, toxicology and microbiological study. A low part of autopsies remain without a conclusive cause of death, but all these cases are usually seen in young population, apparently healthy who died suddenly and unexpectedly. In these situations a cardiac arrhythmia is suspected as cause of death and genetic testing is recommended despite not regularly performed. Sudden death is a natural and unexpected decease that occurs in apparently healthy people, or whose disease was not severe enough to expect a fatal outcome. It can be due to several pathologies, usually of cardiac cause and called sudden cardiac death. In infants and young people, both long QT syndrome and catecholaminergic polymorphic ventricular tachycardia are main causes in negative autopsies. These genetic diseases lead to ventricular fibrillation, syncope and sudden cardiac death in a normal heart. Unfortunately, sudden cardiac death could be the first manifestation of the diseases, being early identification and prevention a crucial point in current medical practice. This chapter focuses on sudden death and negative autopsy in young population, mainly due to cardiac arrhythmias.",book:{id:"6262",slug:"post-mortem-examination-and-autopsy-current-issues-from-death-to-laboratory-analysis",title:"Post Mortem Examination and Autopsy",fullTitle:"Post Mortem Examination and Autopsy - Current Issues From Death to Laboratory Analysis"},signatures:"Georgia Sarquella-Brugada, Sergi Cesar, Anna Fernandez-Falgueras,\nMaria Dolores Zambrano, Anna Iglesias, Josep Brugada, Ramon\nBrugada and Oscar Campuzano",authors:[{id:"54165",title:"Prof.",name:"Ramon",middleName:null,surname:"Brugada",slug:"ramon-brugada",fullName:"Ramon Brugada"},{id:"54168",title:"Dr.",name:"Oscar",middleName:null,surname:"Campuzano",slug:"oscar-campuzano",fullName:"Oscar Campuzano"},{id:"218478",title:"Dr.",name:"Georgia",middleName:null,surname:"Sarquella-Brugada",slug:"georgia-sarquella-brugada",fullName:"Georgia Sarquella-Brugada"},{id:"218479",title:"Dr.",name:"Sergi",middleName:null,surname:"Cesar",slug:"sergi-cesar",fullName:"Sergi Cesar"},{id:"218480",title:"MSc.",name:"Anna",middleName:null,surname:"Fernandez-Falgueras",slug:"anna-fernandez-falgueras",fullName:"Anna Fernandez-Falgueras"},{id:"218482",title:"Dr.",name:"Maria Dolores",middleName:null,surname:"Zambrano",slug:"maria-dolores-zambrano",fullName:"Maria Dolores Zambrano"},{id:"218483",title:"MSc.",name:"Anna",middleName:null,surname:"Iglesias",slug:"anna-iglesias",fullName:"Anna Iglesias"},{id:"218484",title:"Prof.",name:"Josep",middleName:null,surname:"Brugada",slug:"josep-brugada",fullName:"Josep Brugada"}]},{id:"57778",title:"Defining Dental Age for Chronological Age Determination",slug:"defining-dental-age-for-chronological-age-determination",totalDownloads:2574,totalCrossrefCites:1,totalDimensionsCites:3,abstract:"Dental age assessment is one of the most reliable methods of chronological age estimation used for criminal, forensic and anthropologic purposes. Visual, radiographic, chemical and histological techniques can be used for dental age estimation. Visual method is based on the sequence of eruption of the teeth and morphological changes that are caused due to function such as attrition, changes in color that are indicators of aging. Radiographs of the dentition can be used to determine the stage of dental development of the teeth from initial mineralization of a tooth, crown formation to root apex maturation. Histological methods require the preparation of the tissues for detailed microscopic examination. The chemical analysis of dental hard tissues determines alterations in ion levels with age, whereas the histological and chemical methods are invasive methods requiring extraction/sectioning of the tooth. In this chapter, the different techniques and considered studies were overviewed in conjunction with their advantages and disadvantages. It needs to be taken into consideration that rather than restricting on one age estimation technique, using the other available techniques additionally and performing repetitive measurements may be beneficial for accurate age estimation.",book:{id:"6262",slug:"post-mortem-examination-and-autopsy-current-issues-from-death-to-laboratory-analysis",title:"Post Mortem Examination and Autopsy",fullTitle:"Post Mortem Examination and Autopsy - Current Issues From Death to Laboratory Analysis"},signatures:"Fatma Deniz Uzuner, Emine Kaygısız and Nilüfer Darendeliler",authors:[{id:"172009",title:"Dr.",name:"Fatma Deniz",middleName:null,surname:"Uzuner",slug:"fatma-deniz-uzuner",fullName:"Fatma Deniz Uzuner"},{id:"200985",title:"Dr.",name:"Emine",middleName:null,surname:"Kaygisiz",slug:"emine-kaygisiz",fullName:"Emine Kaygisiz"},{id:"222232",title:"Prof.",name:"Nilufer",middleName:null,surname:"Darendeliler",slug:"nilufer-darendeliler",fullName:"Nilufer Darendeliler"}]},{id:"77222",title:"Forensic Analysis and Interpretation of Tool Marks",slug:"forensic-analysis-and-interpretation-of-tool-marks",totalDownloads:492,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"The forensic analysis and interpretation of tool marks raise for consideration key methods and advances in the field of tool marks in forensic science. This chapter shows how tool mark analysis can be utilized in the course of criminal investigations. The focus of the chapter is on bringing together as much scientific knowledge in the area as possible in an accessible manner. It covers all aspects of tool mark evidence from the crime scene to the courtroom. This chapter provides information about tool marks in an effort to assist tool mark examiners as well as people practicing forensic science, crime scene examiners, crime investigating officers and members of the legal profession. 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