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.

单细胞多模式方法正在改变人类造血祖细胞区室的分析。通过测序 (CITE-seq) 对转录组和表位进行细胞索引，可以耦合表面蛋白和转录组分析，从而揭示祖细胞状态下的基因组程序。为了在原代人骨髓细胞上系统地执行 CITE-seq，我们使用 266 种 CITE-seq 抗体（抗体衍生标签）进行滴定，并使用机器学习来优化一组 132 种抗体。多模式分析解析了超过 80 个由独特的表面标记和转录组定义的干细胞、祖细胞、免疫细胞、基质细胞和移行细胞。该数据集为计算机预测的细胞状态提供了流式细胞术解决方案，并识别了跨越种族和性别的供体中一致检测到的数十种细胞表面标记。最后，根据该图谱的注释，我们为临床反应不同的急性髓系白血病干细胞群指定了正常骨髓当量。该图谱是人类健康和疾病造血祖细胞分析的先进数字资源。