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Recent lineage tracing based single-cell techniques (LT-scSeq), e.g., the Lineage And RNA RecoverY (LARRY) barcoding system, have enabled clonally resolved interpretation of differentiation trajectories. However, the heterogeneity of clone-specific kinetics remains understudied, both quantitatively and in terms of interpretability, thus limiting the power of barcoding systems to unravel how heterogeneous stem cell clones drive the overall cell population dynamics. Here, we present CLADES, a NeuralODE-based framework to faithfully estimate the clone and population-specific kinetics from both newly generated and publicly available LARRY LT-scSeq data. By incorporating a stochastic simulation algorithm (SSA) and differential expression gene (DEGs) analysis, CLADES yields the summary of cell division dynamics across differentiation time-courses and reconstructs the lineage tree of the progenitor cells in a quantitative way. Moreover, clone-level behaviors can be grouped into characteristic types by pooling individual clones into meta-clones for analyses at various resolutions. Finally, we show that meta-clone specific cellular behaviors identified by CLADES originate from hematopoietic stem and progenitor cells in distinct transcriptional states. In conclusion, we report a scalable approach to robustly quantify clone-specific differentiation kinetics of cellular populations for time-series systems with static barcoding designs. Recent studies have traced haematopoiesis at the clonal level but lack a way to extract dynamical information. Here, authors develop CLADES, a tool to estimate cellular kinetics and the number of divisions to produce mature cells for each clone, in human cord blood and adult mouse haematopoiesis.

The tumor microenvironment encompasses various innate immune cells which regulate tumor progression. Exploiting innate immune cells is a new frontier of cancer immunotherapy. However, the classical surface markers for cell-type classification cannot always well-conclude the phenotype, which will further hinge our understanding. The innate immune cells include dendritic cells, monocytes/macrophages, natural killer cells, and innate lymphoid cells. They play important roles in tumor growth and survival, in some cases promoting cancer, in other cases negating cancer. The precise characterization of innate immune cells at the single-cell level will boost the potential of cancer immunotherapy. With the development of single-cell RNA sequencing technology, the transcriptome of each cell in the tumor microenvironment can be dissected at a single-cell level, which paves a way for a better understanding of the cell type and its functions. Here, we summarize the subtypes and functions of innate immune cells in the tumor microenvironment based on recent literature on single-cell technology. We provide updates on recent achievements and prospects for how to exploit novel functions of tumor-associated innate immune cells and target them for cancer immunotherapy.

Programmed death ligand 1 (PD-L1) serves as a pivotal immune checkpoint in both the innate and adaptive immune systems. PD-L1 is expressed in macrophages in response to IFNγ. We examined whether PD-L1 might regulate macrophage development. We established PD-L1 KO ( CD274 -/- ) human pluripotent stem cells and differentiated them into macrophages and observed a 60% reduction in CD11B + CD45 + macrophages in CD274 -/- ; this was orthogonally verified, with the PD-L1 inhibitor BMS-1166 reducing macrophages to the same fold. Single-cell RNA sequencing further confirmed the down-regulation of the macrophage-defining transcription factors SPI1 and MAFB . Furthermore, CD274 -/- macrophages reduced the level of inflammatory signals such as NF-κB and TNF, and chemokine secretion of the CXCL and CCL families. Anti-inflammatory TGF-β was up-regulated. Finally, we identified that CD274 -/- macrophages significantly down-regulated interferon-stimulated genes despite the presence of IFNγ in the differentiation media. These data suggest that PD-L1 regulates inflammatory programs of macrophages from human pluripotent stem cells.

Cytoplasmic dynein 1 is fundamentally important for transporting a variety of essential cargoes along microtu bules within eukaryotic cells. However, in mammals, few mutants are available for studying the effects of defects in dyneincontrolled processes in the context of the whole organism. Here, we deleted mouse Dlicl gene encoding DLICI, a subunit of the dynein complex. DlicF/ mice are viable, but display severe photoreceptor degeneration. Ab lation of Dlicl results in ectopic accumulation of outer segment （OS） proteins, and impairs OS growth and ciliogen esis of photoreceptors by interfering with Rabllvesicle trafficking and blocking efficient OS protein transport from Golgi to the basal body. Our studies show that Dlicl deficiency partially blocks vesicle export from endoplasmic re ticulum （ER）, but seems not to affect vesicle transport from the ER to Golgi. Further mechanistic study reveals that lack of Dlicl destabilizes dynein subunits and alters the normal subcellular distribution of dynein in photoreceptors, probably due to the impaired transport function of dynein. Our results demonstrate that Dlicl plays important roles in ciliogenesis and protein transport to the OS, and is required for photoreceptor development and survival. The Dlicl/ mice also provide a new mouse model to study human retinal degeneration.

The ability to perform lineage tracing at the single-cell level is critical to reconstructing dynamic transitions during cell differentiation. However, prospective tracing approaches inevitably encounter outstanding challenges including barcoding precision, barcode diversity, and detection efficiency, which can skew inferred lineage relationships. Human pluripotent stem cells (hPSC) even face risks of DNA-damage-induced toxicity-related cell death. We explored the use of naturally occurring somatic mutations in mitochondrial transcripts detected in single-cell RNA-seq as genetic lineage barcodes in HPSC. In this study, we used an enrichment of scRNA-seq mitochondrial reads and a robust computational method to identify clonally relevant mitochondrial variants as endogenous genetic barcodes for clonal tracking of early embryonic hematopoiesis from hPSC. We modeled the development of embryonic tissues from hPSCs and delineated the ontogeny of hematopoietic cells by mitochondrial variant lineage tracing. We identified multiple waves of erythropoiesis in time-series scRNA-seq data. We further applied mitochondrial variant lineage tracing to spatial transcriptomics and identified the clonal development of both erythro-myeloid progenitors and their niche cells. Our study highlights the power of mitochondrial variants as an endogenous marker for clonal tracking in modeling human development from stem cells.

Recent lineage tracing single-cell techniques (LT-scSeq), e.g., the Lineage And RNA RecoverY (LARRY) barcoding system, have enabled clonally resolved interpretation of differentiation trajectories. However, the heterogeneity of clone-specific kinetics remains understudied, both quantitatively and in terms of interpretability, thus limiting the power of bar-coding systems to unravel how heterogeneous stem cell clones drive overall cell population dynamics. Here, we present CLADES, a NeuralODE-based framework to faithfully estimate clone-specific kinetics of cell states from newly generated and publicly available human cord blood LARRY LT-scSeq data. By incorporating a stochastic simulation algorithm (SSA) and differential expression gene (DEGs) analysis, CLADES yields cell division dynamics across differentiation timecourses and fate bias predictions for the early progenitor cells. Moreover, clone-level quantitative behaviours can be grouped into characteristic types by pooling individual clones into meta-clones. By benchmarking with CoSpar, we found that CLADES improves fate bias prediction accuracy at the meta-clone level. In conclusion, we report a broadly applicable approach to robustly quantify differentiation kinetics using meta-clones while providing valuable insights into the fate bias of cellular populations for any organ system maintained by a pool of heterogeneous stem and progenitor cells.

Immune cell subsets in the tumor predict prognosis. Identifying reliable subsets consistently in multiple patients is clinically important. What’s more advantageous to the field is if such subsets can be a target of immunotherapy. Here we analyzed single-cell RNA-sequencing datasets of patients with triple-negative breast cancer and identified APOE or FABP5-expressing macrophages correlated with poor prognosis. Further validation with TCGA-BRCA cohort detailed molecular signatures of these macrophages as lipidic and senescent. Our receptor-ligand mapping identified lipidic and senescent macrophages both suppress T-lymphoid and myeloid immunogenicity. Finally, we discovered that immune checkpoint therapy combined with chemotherapy reprogrammed anti-tumor microenvironment enriched with FOLR2+ macrophages facilitated T-cell activations. This suggests that lipidic and senescent macrophages could be a therapeutic target of immune checkpoint therapy.

PD-L1 (programmed death-ligand 1) serves as a pivotal immune checkpoint in both the innate and adaptive immune systems. PD-L1 is expressed in macrophages in response to interferon-gamma (IFNgamma). We examined whether PD-L1 might regulate macrophage development. We established PD-L1-/- human pluripotent stem cells, differentiated them into macrophages, and observed a 60% reduction of CD11B+CD45+ macrophages in PD-L1-/-, orthogonally verified with PD-L1 inhibitor BMS-1166 reduced macrophages to the same fold. Single-cell RNA sequencing further confirmed the 60% reduction of macrophages as well as the down-regulation of macrophage-defining transcription factors SPI1, KLF6, and MAFB. Further, PD-L1-/- macrophages reduced the level of inflammatory signals such as NFkappaB, TNF, and chemokines CXCL and CCL families. Whilst anti-inflammatory TGF-beta was upregulated. Finally, we identified that PD-L1-/- macrophages significantly down-regulated interferon-stimulated genes (ISGs) despite IFNgamma in differentiation media. Mechanistically, PD-L1-/- macrophages reduced IFNGR1 expression explaining that cells could not respond to IFNgamma. These data suggest that PD-L1 regulates inflammatory macrophage development by maintaining the IFNgamma signal.Competing Interest StatementThe authors have declared no competing interest.Footnotes* https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE218722

Single-cell level characterization of embryonic development is a major benchmark of human developmental biology. Spatiotemporal analysis of stem-cell-derived embryos offers conceptual and technical advances in the field. Here, we defined the single-cell spatiotemporal gene expression landscape of human embryonic development with stem-cell-derived organoids. We established the human embryonic organoid (HEMO) from expanded potential stem cells and achieved both embryonic and extraembryonic tissues in the same organoid. Time-series single-cell RNA sequencing paired with single-cell resolution spatial revealed human embryonic development signatures such as extraembryonic placenta, yolk sac hematopoiesis neural crest, blood vessels, and cardiac mesoderm. Hematopoietic tissues eventually predominated HEMO with erythropoiesis, mekagaryopiesis, and myelopoiesis. Cell-cell communication network analysis demonstrated that trophoblast-like tissues supplied WNT signaling in neural crest cells to facilitate maturation and migration. Single-cell resolution spatial transcriptomics defined the yolk sac erythro-megakaryopoietic niche. Vitronectin-integrin signaling, a major contributor to megakaryocyte maturation, was predominant in the yolk sac niche in HEMO and to human fetal samples. Overall, our study advances the spatiotemporal analysis of human embryonic development in stem-cell-derived organoids. Competing Interest Statement The authors have declared no competing interest. Footnotes * https://github.com/CHAOYiming/HEMO_analysis