Multiparameter analysis of timelapse imaging reveals kinetics of megakaryocytic erythroid progenitor clonal expansion and differentiation.

Single-cell assays have enriched our understanding of hematopoiesis and, more generally, stem and progenitor cell biology. However, these single-end-point approaches provide only a static snapshot of the state of a cell. To observe and measure dynamic changes that may instruct cell fate, we developed an approach for examining hematopoietic progenitor fate specification using long-term (> 7-day) single-cell time-lapse imaging for up to 13 generations with in situ fluorescence staining of primary human hematopoietic progenitors followed by algorithm-assisted lineage tracing. We analyzed progenitor cell dynamics, including the division rate, velocity, viability, and probability of lineage commitment at the single-cell level over time. We applied a Markov probabilistic model to predict progenitor division outcome over each generation in culture. We demonstrated the utility of this methodological pipeline by evaluating the effects of the cytokines thrombopoietin and erythropoietin on the dynamics of self-renewal and lineage specification in primary human bipotent megakaryocytic-erythroid progenitors (MEPs). Our data support the hypothesis that thrombopoietin and erythropoietin support the viability and self-renewal of MEPs, but do not affect fate specification. Thus, single-cell tracking of time-lapse imaged colony-forming unit assays provides a robust method for assessing the dynamics of progenitor self-renewal and lineage commitment.

Dissecting mammalian spermatogenesis using spatial transcriptomics.

Single-cell RNA sequencing has revealed extensive molecular diversity in gene programs governing mammalian spermatogenesis but fails to delineate their dynamics in the native context of seminiferous tubules, the spatially confined functional units of spermatogenesis. Here, we use Slide-seq, a spatial transcriptomics technology, to generate an atlas that captures the spatial gene expression patterns at near-single-cell resolution in the mouse and human testis. Using Slide-seq data, we devise a computational framework that accurately localizes testicular cell types in individual seminiferous tubules. Unbiased analysis systematically identifies spatially patterned genes and gene programs. Combining Slide-seq with targeted in situ RNA sequencing, we demonstrate significant differences in the cellular compositions of spermatogonial microenvironment between mouse and human testes. Finally, a comparison of the spatial atlas generated from the wild-type and diabetic mouse testis reveals a disruption in the spatial cellular organization of seminiferous tubules as a potential mechanism of diabetes-induced male infertility.