Analysis of the human hematopoietic progenitor compartment is being transformed by single-cell multimodal approaches. Cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) enables coupled surface protein and transcriptome profiling, thereby revealing genomic programs underlying progenitor states. To perform CITE-seq systematically on primary human bone marrow cells, we used titrations with 266 CITE-seq antibodies (antibody-derived tags) and machine learning to optimize a panel of 132 antibodies. Multimodal analysis resolved >80 stem, progenitor, immune, stromal and transitional cells defined by distinctive surface markers and transcriptomes. This dataset enables flow cytometry solutions for in silico-predicted cell states and identifies dozens of cell surface markers consistently detected across donors spanning race and sex. Finally, aligning annotations from this atlas, we nominate normal marrow equivalents for acute myeloid leukemia stem cell populations that differ in clinical response. This atlas serves as an advanced digital resource for hematopoietic progenitor analyses in human health and disease.

Alternative splicing is one of the primary mechanisms used to achieve mRNA transcript and proteomic diversity in higher order eukaryotes. In cancer, altered mRNA splicing can lead to aberrant protein products that promote oncogenic transformation, metastasis and confer chemotherapy resistance, due to splicing factor mutation or mis-expression. We hypothesize that the role of splicing imbalance in cancer is grossly underestimated and is likely regulated by the same recurrent global disruptions observed across human cancers. To determine common and malignancy specific splicing subtypes across cancer we designed a novel integrated computational workflow that uses genome variant data from RNA-Seq in conjunction with fully unsupervised analyses (NMF and SVM) to identify novel patient splicing-defined subtypes (OncoSplice). Applied to 20 adult and pediatric cancers with large cohorts, we identified common-recurrent splicing subtypes associated with MYC-hyperactivation and TGF-beta signaling, lineage reprogramming, tumor infiltration and broad mutation impacts. Such recurrent and tumor specific subtypes were frequently associated with poor prognosis and novel dominantly regulated driving splicing events (e.g., transcription factors). Broad splicing subtypes were associated with circadian dysregulation, as determined from normal healthy tissues (GTEx) and in many cases were found to phenocopy well-described splicing mutation impacts or were associated with new RNA-binding proteins (RBPs) evidenced by orthogonal computational predictions. Splicing subtype associated RBPs were frequently undergo autoregulation, as further evidenced by CLIP-Seq and RBP knockdown. To enhance the RNA community’s ability to explore hundreds of known and novel splicing variation across cancers and healthy tissues, we provide an interactive online splicing explorer at oncosplice.org. Together, these data highlight previously unknown regulatory relationships and prognostic associations in cancer associated with broad and targeted splicing regulation. Citation Format: Anukana Bhattacharjee, Audrey Crowther, Guangyuan Li, Meenakshi Venkatasubramanian, Dan Schnell, Stuart Hay, Preeti Singh, Krithika R. Subramanian, Kashish Chetal, Xiaoting Chen, Aishwarya Kulkarni, Matthew T. Weirauch, Nathan Salomonis. Pan-cancer splicing analysis reveals shared drivers of malignant transformation and survival [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3139.

Plasma cell (PC) precursors are generated during a germinal center (GC) response and migrate to the bone marrow (BM) where they undergo terminal differentiation into short-lived (SL) or long-lived (LL) antibody secreting cells. The developmental dynamics and genomic states of PC precursors remain to be elucidated1. We utilize a novel experimental system, involving CD138+ splenocytes isolated from NP-KLH immunized C57Bl/6J mice (21–42 dpi) and their adoptive transfer into B cell deficient mMT mice, to analyze developmental dynamics of antigen-specific PC precursors and their progeny at single-cell resolution. We demonstrate that SLPC precursors are generated earlier (d21), whilst LLPC precursors are generated later (d35) during a GC response. scRNA-seq analyses reveals increased frequency of a novel cluster of transitional cells expressing both B-lineage and plasma cell-specific genes. Coupled analyses using BCR-seq shows that NP-specific transitional cells express cell cycle genes and undergo greater clonal expansion on d35 to give rise to proliferating and quiescent PCs. Antigen-specific clonal tracking in the spleen and BM compartments suggests that the transitional cells emanating from the GC, differentiate into proliferating PCs before migrating to the BM. CITE-seq analyses and reconstitution experiments demonstrate that proliferating PCs are contained within the B220− CD138+ CD44+ CD11a+ subset and are enriched for BMPC precursors. These results reveal novel GC-derived transitional cells that generate precursors of both SLPCs and LLPCs with differing developmental and proliferation dynamics in a humoral immune response.

Advances in genetics and sequencing have identified a plethora of disease-associated and disease-causing genetic alterations. To determine causality between genetics and disease, accurate models for molecular dissection are required; however, the rapid expansion of transcriptional populations identified through single-cell analyses presents a major challenge for accurate comparisons between mutant and wild-type cells. Here we generate mouse models of human severe congenital neutropenia (SCN) using patient-derived mutations in the GFI1 transcription factor. To determine the effects of SCN mutations, we generated single-cell references for granulopoietic genomic states with linked epitopes(1), aligned mutant cells to their wild-type equivalents and identified differentially expressed genes and epigenetic loci. We find that GFI1-target genes are altered sequentially, as cells go through successive states of differentiation. These insights facilitated the genetic rescue of granulocytic specification but not post-commitment defects in innate immune effector function, and underscore the importance of evaluating the effects of mutations and therapy within each relevant cell state. Mouse models of severe congenital neutropenia using patient-derived mutations in the GFI1 locus are used to determine the mechanisms by which the disease progresses.

To understand the molecular pathogenesis of human disease, precision analyses to define alterations within and between disease-associated cell populations are desperately needed. Single-cell genomics represents an ideal platform to enable the identification and comparison of normal and diseased transcriptional cell populations. We created cellHarmony, an integrated solution for the unsupervised analysis, classification, and comparison of cell types from diverse single-cell RNA-Seq datasets. cellHarmony efficiently and accurately matches single-cell transcriptomes using a community-clustering and alignment strategy to compute differences in cell-type specific gene expression over potentially dozens of cell populations. Such transcriptional differences are used to automatically identify distinct and shared gene programs among cell-types and identify impacted pathways and transcriptional regulatory networks to understand the impact of perturbations at a systems level. cellHarmony is implemented as a python package and as an integrated workflow within the software AltAnalyze. We demonstrate that cellHarmony has improved or equivalent performance to alternative label projection methods, is able to identify the likely cellular origins of malignant states, stratify patients into clinical disease subtypes from identified gene programs, resolve discrete disease networks impacting specific cell-types, and illuminate therapeutic mechanisms. Thus, this approach holds tremendous promise in revealing the molecular and cellular origins of complex disease.

The developing kidney provides a useful model for study of the principles of organogenesis. In this report we use three independent platforms, Drop-Seq, Chromium 10x Genomics and Fluidigm C1, to carry out single cell RNA-Seq (scRNA-Seq) analysis of the E14.5 mouse kidney. Using the software AltAnalyze, in conjunction with the unsupervised approach ICGS, we were unable to identify and confirm the presence of 16 distinct cell populations during this stage of active nephrogenesis. Using a novel integrative supervised computational strategy, we were able to successfully harmonize and compare the cell profiles across all three technological platforms. Analysis of possible cross compartment receptor/ligand interactions identified the nephrogenic zone stroma as a source of GDNF. This was unexpected because the cap mesenchyme nephron progenitors had been thought to be the sole source of GDNF, which is a key driver of branching morphogenesis of the collecting duct system. The expression of Gdnf by stromal cells was validated in several ways, including Gdnf in situ hybridization combined with immunohistochemistry for SIX2, and marker of nephron progenitors, and MEIS1, a marker of stromal cells. Finally, the single cell gene expression profiles generated in this study confirmed and extended previous work showing the presence of multilineage priming during kidney development. Nephron progenitors showed stochastic expression of genes associated with multiple potential differentiation lineages.

Efforts to understand the genomic impact of human-disease-relevant genetic lesions and how they disrupt the normal sequence of cell-state transitions is hampered by a lack of defined hierarchical cellular states and corresponding networks of regulatory genes (transcription factors). Severe congenital neutropenia (SCN) patients display inherited and de novo mutations in Growth factor independent-1 (GFI1), which encodes a zinc-finger transcription factor. We identified known (e.g. N382S in zinc finger 5) and novel GFI1 sequence changes in SCN patients, then used lentiviral mediated expression to functionally evaluate them. GFI1-N382S, GFI1-K403R and GFI1-R412X mutations (in zinc finger 6) significantly elevated the expression of the Gfi1 target gene, Irf8. We generated mice with these patient-derived SCN-associated mutations in the murine Gfi1 locus. Neonatal and adult Gfi1N382S/- and Gfi1R412X/- mice are neutropenic, but Gfi1K403R/- mice have normal steady-state neutrophil levels. The resulting steady-state dysgranulopoiesis in adult mice was further pronounced in neonates. We noted that Gfi1R412X/- mice accumulate less Gfi1 protein than Gfi1+/+, while Gfi1R412X/R412X homozygous alleles genetically rescued both the hypomorphic protein defect and substantially restored neutrophil numbers (though not to normal). In contrast, functional challenge with neutrophil-dependent pathogens in vivo revealed a broad susceptibility for all Gfi1-mutant mice.

The Human Cell Atlas (HCA) is expected to facilitate the creation of reference cell profiles, marker genes, and gene regulatory networks that will provide a deeper understanding of healthy and disease cell types from clinical biospecimens. The hematopoietic system includes dozens of distinct, transcriptionally coherent cell types, including intermediate transitional populations that have not been previously described at a molecular level. Using the first data release from the HCA bone marrow tissue project, we resolved common, rare, and potentially transitional cell populations from over 100,000 hematopoietic cells spanning 35 transcriptionally coherent groups across eight healthy donors using emerging new computational approaches. These data highlight novel mixed-lineage progenitor populations and putative trajectories governing granulocytic, monocytic, lymphoid, erythroid, megakaryocytic, and eosinophil specification. Our analyses suggest significant variation in cell-type composition and gene expression among donors, including biological processes affected by donor age. To enable broad exploration of these findings, we provide an interactive website to probe intra-cell and extra-cell population differences within and between donors and reference markers for cellular classification and cellular trajectories through associated progenitor states.