Engineering T Cell Development for the Next Generation of Stem Cell-Derived Immunotherapies

Michaels, Yale S.; Durland, Lauren J.; Zandstra, Peter W.

doi:10.1089/genbio.2023.0008

Cited by 6 publications

(4 citation statements)

References 138 publications

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“…In this investigation, we leveraged an in vitro PSC differentiation system as a tool for unravelling the gene expression programs and lineage hierarchy at the core of T cell specification. We note that this system does not replicate all aspects of the complex microenvironments where HSPC and T cells emerge 5 . However, this system presents substantial benefits, effectively capturing the minimum essential signals required for development, thereby enabling the study of lymphopoiesis in an isogenic and scalable context 22 .…”

Section: Discussionmentioning

confidence: 86%

“…MT-LOT method was only applied for time pairs (4,5) and (9,10). For the remaining time pairs, the package WaddingtonOT computed the transport maps without applying specific multicellular filters.…”

Section: Wot Transition Mapmentioning

confidence: 99%

“…As a lineage barcode was not detected for a subset of the cells sampled at these time points, CoSpar was run with the parameter extend_Tmap_space=True to obtain a trajectory coupling which included all cells sampled. Otherwise the default parameters of the algorithm were used to obtain the results, after tuning experiments (random sampling) determined no improvement from changing the values (defaults were: smooth_array= [15,10,5], CoSpar_KNN=20, sparsity_threshold=0.1, intraclone_threshold=0.05, normalization_mode=1, trunca_threshold=[0.001, 0.01]). The other two sub-methods of CoSpar (one using gene expression only, one using lineage barcodes observed at a single time and gene expression) were also tested but were not considered in our final analysis as the results were judged to be less accurate.…”

Section: Cospar Analysismentioning

confidence: 99%

“…They are also being used to treat cancer in the clinic 1 and have tremendous promise for treating infection, autoimmunity and cardiovascular disease [2][3][4] . Understanding the gene regulatory programs and cell fate decisions that govern T cell development will not only improve our fundamental understanding of this crucial developmental process, but will also guide efforts to differentiate T cells in vitro, unlocking an unlimited supply of T cells for therapy 5 .…”

Section: Introductionmentioning

confidence: 99%

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Time- and lineage-resolved transcriptional profiling uncovers gene expression programs and clonal relationships that underlie human T lineage specification

Michaels,

Major,

Bonham-Carter

et al. 2023

Preprint

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T cells develop from multi-potent hematopoietic progenitors in the thymus and provide adaptive protection against pathogens and cancer. However, the emergence of human T cell-competent blood progenitors, and their subsequent specification to the T lineage, has been challenging to capture in real time. Here, we leveraged a pluripotent stem cell differentiation system to understand the transcriptional dynamics and cell fate restriction events that underlie this critical developmental process. Time-resolved single cell RNA sequencing revealed that cell-cycle exit, downregulation of the multipotent hematopoietic program, and upregulation of >90 lineage-associated transcription factors all occur within a highly co-ordinated and narrow developmental window. Computational gene-regulatory network inference elucidated the transcriptional logic of T lineage specification, uncovering an important role for YBX1. We mapped the differentiation cell fate hierarchy using transcribed lineage barcoding and mathematical trajectory inference and discovered that mast and myeloid potential bifurcate from each other early in haematopoiesis, upstream of T lineage restriction. Collectively, our analyses provide a quantitative, time-resolved model of human T cell specification with relevance for regenerative medicine and developmental immunology.

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Section: Discussionmentioning

confidence: 86%