Single-cell measurement techniques can now probe gene expression in heterogeneous cell populations from the human body across a range of environmental and physiological conditions. However, new mathematical and computational methods are required to represent and analyze gene-expression changes that occur in complex mixtures of single cells as they respond to signals, drugs, or disease states. Here, we introduce a mathematical modeling platform, PopAlign, that automatically identifies subpopulations of cells within a heterogeneous mixture and tracks gene-expression and cell-abundance changes across subpopulations by constructing and comparing probabilistic models. Probabilistic models provide a low-error, compressed representation of single-cell data that enables efficient large-scale computations. We apply PopAlign to analyze the impact of 40 different immunomodulatory compounds on a heterogeneous population of donor-derived human immune cells as well as patient-specific disease signatures in multiple myeloma. PopAlign scales to comparisons involving tens to hundreds of samples, enabling large-scale studies of natural and engineered cell populations as they respond to drugs, signals, or physiological change.

Single-cell measurement techniques can now probe gene expression in heterogeneous cell populations from the human body across a range of environmental and physiological conditions. However, new mathematical and computational methods are required to represent and analyze gene expression changes that occur in complex mixtures of single cells as they respond to signals, drugs, or disease states. Here, we introduce a mathematical modeling platform, PopAlign, that automatically identifies subpopulations of cells within a heterogeneous mixture, and tracks gene expression and cell abundance changes across subpopulations by constructing and comparing probabilistic models. We apply PopAlign to analyze the impact of 42 different immunomodulatory compounds on a heterogeneous population of donor-derived human immune cells as well as patient-specific disease signatures in multiple myeloma. PopAlign scales to comparisons involving tens to hundreds of samples, enabling large-scale studies of natural and engineered cell populations as they respond to drugs, signals or physiological change.

Gonad development is an exciting model to study cell fate commitment. However, the specification and differentiation of somatic cell lineages within the testis and the ovary are incompletely characterized, especially in humans. In fact, a better understanding of sex determination first requires the identification of all the cell types involved and of their dynamic expression programs. Here we present a comprehensive analysis of approximately 128,000 single cells collected from 33 fetal testes and ovaries between 5 and 12 postconceptional weeks. In particular, a focused analysis of somatic cells allowed us to identify a common population of bipotential progenitors derived from the coelomic epithelium of both male and female gonads and capable of committing to either a steroidogenic or a supporting fate. Moreover, we have shown that early supporting cells, prior to further differentiation into Sertoli or granulosa cells, also give rise to the rete testis/ovarii lineage. Finally, we found that the ovary retains the capacity to feed the supporting cell pool for an extended period of time, directly from the surface epithelial cells and, bypassing the bipotential progenitor step. Altogether, our results provide an unprecedented revisiting of the human gonadal sex determination process.