We analyzed 77 nonproductive and 574 productive human VHDJH rearrangements with a newly developed program, JOINSOLVER. In the productive repertoire, the H chain complementarity determining region 3 (CDR3H) was significantly shorter (46.7 ± 0.5 nucleotides) than in the nonproductive repertoire (53.8 ± 1.9 nucleotides) because of the tendency to select rearrangements with less TdT activity and shorter D segments. Using criteria established by Monte Carlo simulations, D segments could be identified in 71.4% of nonproductive and 64.4% of productive rearrangements, with a mean of 17.6 ± 0.7 and 14.6 ± 0.2 retained germline nucleotides, respectively. Eight of 27 D segments were used more frequently than expected in the nonproductive repertoire, whereas 3 D segments were positively selected and 3 were negatively selected, indicating that both molecular mechanisms and selection biased the D segment usage. There was no bias for D segment reading frame (RF) use in the nonproductive repertoire, whereas negative selection of the RFs encoding stop codons and positive selection of RF2 that frequently encodes hydrophilic amino acids were noted in the productive repertoire. Except for serine, there was no consistent selection or expression of hydrophilic amino acids. A bias toward the pairing of 5′ D segments with 3′ JH segments was observed in the nonproductive but not the productive repertoire, whereas VH usage was random. Rearrangements using inverted D segments, DIR family segments, chromosome 15 D segments and multiple D segments were found infrequently. Analysis of the human CDR3H with JOINSOLVER has provided comprehensive information on the influences that shape this important Ag binding region of VH chains.

Single cell sequencing is transforming many fields of science but the vast amount of data it creates has the potential to both illuminate and obscure underlying biology. To harness the exciting potential of single cell data for the study of the mouse spinal cord, we have created a harmonized atlas of spinal cord transcriptomic cell types that unifies six independent and disparate studies into one common analysis. With the power of this large and diverse dataset, we reveal spinal cord cell type organization, validate a combinatorial set of markers for in-tissue spatial gene expression analysis, and optimize the computational classification of spinal cord cell types based on transcriptomic data. This work provides a comprehensive resource with unprecedented resolution of spinal cord cell types and charts a path forward for how to utilize transcriptomic data to expand our knowledge of spinal cord biology.

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