Dysfunctional immune response in the COVID-19 patients is a recurrent theme impacting symptoms and mortality, yet the detailed understanding of pertinent immune cells is not complete. We applied single-cell RNA sequencing to 284 samples from 196 COVID-19 patients and controls and created a comprehensive immune landscape with 1.46 million cells. The large dataset enabled us to identify that different peripheral immune subtype changes were associated with distinct clinical features including age, sex, severity, and disease stages of COVID-19. SARS-CoV-2 RNAs were found in diverse epithelial and immune cell types, accompanied by dramatic transcriptomic changes within viral positive cells. Systemic up-regulation of S100A8/A9, mainly by megakaryocytes and monocytes in the peripheral blood, may contribute to the cytokine storms frequently observed in severe patients. Our data provide a rich resource for understanding the pathogenesis and developing effective therapeutic strategies for COVID-19.

We present a computational model to study the spatio-temporal dynamics of epidermis homoeostasis under normal and pathological conditions. The model consists of a population kinetics model of the central transition pathway of keratinocyte proliferation, differentiation and loss and an agent-based model that propagates cell movements and generates the stratified epidermis. The model recapitulates observed homoeostatic cell density distribution, the epidermal turnover time and the multilayered tissue structure. We extend the model to study the onset, recurrence and phototherapy-induced remission of psoriasis. The model considers psoriasis as a parallel homoeostasis of normal and psoriatic keratinocytes originated from a shared stem cell (SC) niche environment and predicts two homoeostatic modes of psoriasis: a disease mode and a quiescent mode. Interconversion between the two modes can be controlled by interactions between psoriatic SCs and the immune system and by normal and psoriatic SCs competing for growth niches. The prediction of a quiescent state potentially explains the efficacy of multi-episode UVB irradiation therapy and recurrence of psoriasis plaques, which can further guide designs of therapeutics that specifically target the immune system and/or the keratinocytes.

Single-cell RNA sequencing provides exciting opportunities to unbiasedly study hematopoiesis. However, our understanding of leukemogenesis was limited due to the high individual differences. Integrated analyses of hematopoiesis and leukemogenesis potentially provides new insights. Here we analyzed ~200,000 single-cell transcriptomes of bone marrow mononuclear cells (BMMCs) and its subsets from 23 clinical samples. We constructed a comprehensive cell atlas as hematopoietic reference. We developed counterpart composite index (CCI; available at GitHub: https://github.com/pengfeeei/cci) to search for the healthy counterpart of each leukemia cell subpopulation, by integrating multiple statistics to map leukemia cells onto reference hematopoietic cells. Interestingly, we found leukemia cell subpopulations from each patient had different healthy counterparts. Analysis showed the trajectories of leukemia cell subpopulations were similar to that of their healthy counterparts, indicating that developmental termination of leukemia initiating cells at different phases leads to different leukemia cell subpopulations thus explained the origin of leukemia heterogeneity. CCI further predicts leukemia subtypes, cellular heterogeneity, and cellular stemness of each leukemia patient. Analyses of leukemia patient at diagnosis, refractory, remission and relapse vividly presented dynamics of cell population during leukemia treatment. CCI analyses showed the healthy counterparts of relapsed leukemia cells were closer to the root of hematopoietic tree than that of other leukemia cells, although single-cell transcriptomic genetic variants and haplotype tracing analyses showed the relapsed leukemia cell were derived from an early minor leukemia cell population. In summary, this study developed a unified framework for understanding leukemogenesis with hematopoiesis reference, which provided novel biological and medical implication.

Mesenchymal stem/stromal cells (MSCs) are promising cell sources for regenerative medicine and the treatment of autoimmune disorders. Comparing MSCs from different tissues at the single-cell level is fundamental for optimizing clinical applications. Here we analyzed single-cell RNA-seq data of MSCs from four tissues, namely umbilical cord, bone marrow, synovial tissue, and adipose tissue. We identified three major cell subpopulations, namely osteo-MSCs, chondro-MSCs, and adipo/myo-MSCs, across all MSC samples. MSCs from the umbilical cord exhibited the highest immunosuppression, potentially indicating it is the best immune modulator for autoimmune diseases. MSC subpopulations, with different subtypes and tissue sources, showed pronounced differences in differentiation potentials. After we compared the cell subpopulations and cell status pre-and-post chondrogenesis induction, osteogenesis induction, and adipogenesis induction, respectively, we found MSC subpopulations expanded and differentiated when their subtypes consist with induction directions, while the other subpopulations shrank. We identified the genes and transcription factors underlying each induction at the single-cell level and subpopulation level, providing better targets for improving induction efficiency.

BMMCs contain much more subpopulations and are more active (Extended Data Fig. 1g-1j). HSPCs form a single connected entity on tSNE projectionDue to the limited number of HSPCs in BMMCs (Fig. 1b), CD34+ cells, representing HSPCs, were enriched by fluorescence-activated cell sorting (FACS) for investigating hematopoiesis (Extended Data Fig. 2a). The HSPCs essentially formed a single connected entity extending in several directions on tSNE projection and were clustered into 27 clusters for better understanding of the hematopoietic process ( Fig. 1e). We inferred the cell type of each cluster by checking the expression of hematopoietic lineage specific genes ( Fig. 1e, Extended Data Fig. 2b), such as HSC (EMCN+, THY1+, MEG3+, HES1+, Lin-; cluster 1), megakaryocytic progenitors (MkP) (PF4+, GP9+; clusters 5, 7), early erythroid progenitors (EEP) (APOE+, CD36+, CA1+; clusters 8-11), neutrophil, monocyte and DC progenitors (CSF3R+, MPO+ and LYZ+; clusters 13-20) (Fig. 1f, Extended Data Fig. 2b), lymphoid progenitors (CD79A+, IGHM+, VPREB1+; clusters 21-27) ( Fig. 1F and Extended Data Fig. 2b)Cluster 12 highly expressed mast cell and basophil specific genes including HDC, TPSAB1 and MS4A2, as well as eosinophil specific genes including PRG2 and CPA3 ( Fig. 1f and Extended Data Fig. 2b), which could be recently reported Basophil/Eosinophil/Mast progenitors (Ba/Eo/MaP) 10,16 . We did not detect any cluster with gene expression patterns similar to common myeloid progenitor (CMP) (CD34+, CD38+, CD123+, CD45RA-, CD10-and Lin-), consistent with recent studies showing that CMP is a heterogeneous mixture of erythroid and myeloid primed progenitors 10,17,18 . Moreover, we observed multiple subpopulations within pre-defined MkP, EEP, GMP, Pro-B II and so on (Fig. 1e).The expression levels of many genes are gradually changing along the three EEP populations, among which the expression levels of HBA1, TFRC, GYPA, ALAS2, PLK1 and MKI67 gradually increased as the distance to HSC increase (Extended Data Fig. 2b, 2i, 2j). Overall, HSPCs contain a substantial higher fraction of cells in active cell cycles and cell states than that of other BMMCs (Extended Data Fig. 2c-2h). Interestingly, major early stem and progenitor cells (HSC, MPP, LMPP) are in resting phase while major later progenitors are in active proliferation (Extended Data Fig. 2e, 2f), potentially indicating the early progenitor constitutes the major cell pool for regulating hematopoiesis while later progenitors are in simple transitional states. Continuous hematopoietic lineages with hierarchical structureWe implemented Slingshot 19 and SPRING 20 on HSPCs to conduct pseudo-time inference.Pseudo-time ordering of HSPCs exhibits a tree-like structure in which HSC forms the root, from which seven lineages gradually emerged with a hierarchical structure ( Fig. 2a, 2b, 2c), essentially consistent with the cell lineages base on PCA projection (Extended Data Fig. 3a). The results are consistent with recent reports that hematopoiesis is a continuous process 8-10 , but with...