SUMMARY The generation of distinct hematopoietic cell types, including tissue-resident immune cells, distinguishes fetal from adult hematopoiesis. However, the mechanisms underlying differential cell production to generate a layered immune system during hematopoietic development are unclear. Using an irreversible lineage tracing model, we identify a definitive hematopoietic stem cell (HSC) that supports long-term multilineage reconstitution upon transplantation into adult recipients, but does not persist into adulthood in situ. These HSCs are fully multipotent, yet display both higher lymphoid cell production and greater capacity to generate innate-like B and T lymphocytes as compared to coexisting fetal HSCs and adult HSCs. Thus, these developmentally restricted HSCs define the origin and generation of early lymphoid cells that play essential roles in establishing self-recognition and tolerance, with important implications for understanding autoimmune disease, allergy, and rejection of transplanted organs.

While meta-analysis has demonstrated increased statistical power and more robust estimations in studies, the application of this commonly accepted methodology to cytometry data has been challenging. Different cytometry studies often involve diverse sets of markers. Moreover, the detected values of the same marker are inconsistent between studies due to different experimental designs and cytometer configurations. As a result, the cell subsets identified by existing auto-gating methods cannot be directly compared across studies. We developed MetaCyto for automated meta-analysis of both flow and mass cytometry (CyTOF) data. By combining clustering methods with a silhouette scanning method, MetaCyto is able to identify commonly labeled cell subsets across studies, thus enabling meta-analysis. Applying MetaCyto across a set of ten heterogeneous cytometry studies totaling 2,926 samples enabled us to identify multiple cell populations exhibiting differences in abundance between demographic groups. Software is released to the public through Bioconductor (http://bioconductor.org/packages/release/bioc/html/MetaCyto.html).

While meta-analysis has demonstrated increased statistical power and more robust estimations in studies, the application of this commonly accepted methodology to cytometry data has been challenging. Different cytometry studies often involve diverse sets of markers. Moreover, the detected values of the same marker are inconsistent between studies due to different experimental designs and cytometer configurations. As a result, the cell subsets identified by existing auto-gating methods cannot be directly compared across studies. We developed MetaCyto for automated meta-analysis of both flow and mass cytometry (CyTOF) data. By combining clustering methods with a silhouette scanning method, MetaCyto is able to identify commonly labeled cell subsets across studies, thus enabling meta-analysis. Applying MetaCyto across a set of 10 heterogeneous cytometry studies totaling 2926 samples enabled us to identify multiple cell populations exhibiting differences in abundance between White and Asian adults. Software is released to the public through GitHub (github.com/hzc363/MetaCyto).All rights reserved. No reuse allowed without permission.was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint (which . http://dx.doi.org/10.1101/130948 doi: bioRxiv preprint first posted online Jun. 9, 2017; Main text Meta-analysis of existing data across different studies offers multiple benefits. The aggregated data allows researchers to test hypotheses with increased statistical power. The involvement of multiple independent studies increases the robustness of conclusions drawn. In addition, the complexity of aggregated data allows researchers to test or generate new hypotheses. These benefits have been shown by many studies in areas such as genomics, cancer biology and clinical research and have led to important new biomedical findings [1][2][3][4] . For example, one study showed the correlation between neoantigen abundance in tumors and patient survival by performing meta-analysis of RNA sequencing data from The Cancer Genome Atlas (TCGA) 5 . In another study, meta-analysis of genome-wide association studies identified novel loci that affect the risk of type 1 diabetes 6 .With the recent advances in high-throughput cytometry technologies the immune system can be characterized at the single cell level with up to 45 parameters, minimizing the technical limitations and allowing capture of more valuable information from immunology studies [7][8][9] . Open science initiatives have led to more of this type of research data accessible, and the availability of shared cytometry data, including data from flow cytometry and mass cytometry (CyTOF), is growing exponentially. Notably, the ImmPort database (www.immport.org), a repository for immunology-related research and clinical trials, provides numerous studies with thousands of cytometry datasets 10 . However, meta-analysis of cytometry datasets remains particularly challenging. Differen...