Sidney M. Bell

Hundreds of millions of single cells have been analyzed to date using high throughput transcriptomic methods, thanks to technological advances driving the increasingly rapid generation of single-cell data. This provides an exciting opportunity for unlocking new insights into health and disease, made possible by meta-analysis that span diverse datasets building on recent advances in large language models and other machine learning approaches. Despite the promise of these and emerging analytical tools for analyzing large amounts of data, a major challenge remains the sheer number of datasets and inconsistent format, data models and accessibility. Many datasets are available via unique portals platforms that often lack interoperability. Here, we present CZ CellxGene Discover ( cellxgene.cziscience.com ), a data platform that provides curated and interoperable data. This single-cell data resource, available via a free-to-use online data portal, hosts a growing corpus of community contributed data that spans more than 50 million unique cells. Curated, standardized, and associated with consistent cell-level metadata, this collection of interoperable single-cell transcriptomic data is the largest of its kind. A suite of tools and features enables accessibility and reusability of the data via both computational and visual interfaces to allow researchers to rapidly explore individual datasets and perform cross-corpus analysis. This functionality is enabling meta-analyses of tens of millions of cells across studies and tissues and providing global views of human cells at the resolution of single cells.

Quickly and flexibly exploring high-dimensional datasets, such as scRNAseq data, is underserved but critical for hypothesis generation, dataset annotation, publication, sharing, and community reuse. cellxgene is a highly generalizable, web-based interface for exploring high dimensional datasets along categorical, continuous and spatial dimensions, as well as feature annotation. cellxgene is differentiated by its ability to performantly handle millions of observations, and bridges a critical gap by enabling computational and experimental biologists to iteratively ask questions of private and public datasets. In doing so, cellxgene increases the utility and reusability of datasets across the single-cell ecosystem. The codebase can be accessed at https://github.com/chanzuckerberg/cellxgene . For questions and inquiries, please contact cellxgene@chanzuckerberg.com .

Dengue virus (DENV) exists as four genetically distinct serotypes, each of which is historically assumed to be antigenically uniform. Recent analyses suggest that antigenic heterogeneity may exist within each serotype, but its source, extent and impact remain unclear. Here, we construct a sequence-based model to directly map antigenic change to underlying genetic divergence. We identify 49 specific substitutions and four colinear substitution clusters that robustly predict dengue antigenic relationships. We report moderate antigenic diversity within each serotype, resulting in genotype-specific patterns of heterotypic cross-neutralization. We also quantify the impact of antigenic variation on real-world DENV population dynamics, and find that serotype-level antigenic fitness is a dominant driver of dengue clade turnover. These results provide a more nuanced understanding of the relationship between dengue genetic and antigenic evolution, and quantify the effect of antigenic fitness on dengue evolutionary dynamics.

Dengue virus (DENV) exists as four genetically distinct serotypes, each of which is historically assumed to be antigenically uniform. However, recent analyses suggest that antigenic heterogeneity may exist within each serotype, but its source, extent and impact remain unclear. Here, we construct a sequence-based model to directly map antigenic change to underlying genetic divergence. We identify 49 specific substitutions and four colinear substitution clusters that contribute to dengue antigenic diversity. We report moderate antigenic diversity within each serotype, resulting in variation in genotype-specific patterns of heterotypic cross-neutralization. We also quantify the impact of this antigenic heterogeneity on real-world DENV population dynamics. We find that antigenic fitness mediates fluctuations in DENV clade frequencies, although this appears to be primarily explained by coarser serotype-level antigenic differences. These results provide a more nuanced understanding of dengue antigenic evolution, with important ramifications for vaccine design and epidemic preparedness. Author Summary: Dengue virus (DENV), the causative agent of dengue hemorrhagic fever, exists as four genetically distinct serotypes, DENV1 to DENV4. These serotypes are antigenically distinct: symptomatic reinfection with a homotypic virus is very rare, while reinfection with a heterotypic virus is sometimes associated with severe disease. Until recently, it has been assumed that viruses within each serotype are antigenically uniform. However, specific genotypes within each serotype have been anecdotally associated with varying severity of patient outcomes and epidemic magnitude. One hypothesis is that each serotype contains overlooked, meaningful antigenic diversity. While antigenic cartography conducted on neutralization titers suggests that heterogeneity may exist within each serotype, its source, extent and impact is unclear. Here, we analyze a previously published titer dataset to quantify and characterize the extent of DENV intraserotype antigenic diversity. We map antigenic changes to specific mutations in E , the dengue envelope protein, and interpolate across the alignment to estimate the antigenic distance between pairs of viruses based on their genetic differences. We identify 49 specific substitutions and four colinear substitution clusters that contribute to dengue antigenic evolution. We find that DENV antigenic divergence is tightly coupled to DENV genetic divergence, and is likely a gradual, ongoing process. We report modest but significant antigenic diversity within each serotype of DENV, which may have important ramifications for vaccine design. To understand the impact of this antigenic heterogeneity on real-world DENV population dynamics, we also quantify the extent to which population immunity—accumulated through recent circulation of antigenically similar genotypes—determines the success and decline of DENV clades in a hyperendemic population. We find that antigenic fitness is a key determinant of DENV population turnover, although this appears to be driven by coarser serotype-level antigenic differences. By leveraging both molecular data and real-world population dynamics, these results provide a more nuanced understanding of dengue antigenic evolution, with important ramifications for improving vaccine design and epidemic preparedness.

Vaccination could be an evolutionary pressure on seasonal influenza if vaccines reduce the transmission rates of some (“targeted”) strains more than others. In theory, more vaccinated populations should have a lower prevalence of targeted strains compared to less vaccinated populations. We tested for vaccine-induced selection in influenza by comparing strain frequencies between more and less vaccinated human populations. We defined strains in three ways: first as influenza types and subtypes, next as lineages of type B, and finally as clades of influenza A/H3N2. We detected spatial differences partially consistent with vaccine use in the frequencies of subtypes and types and between the lineages of influenza B, suggesting that vaccines do not select strongly among all these phylogenetic groups at regional scales. We did detect a significantly greater frequency of an H3N2 clade with known vaccine escape mutations in more vaccinated countries during the 2014–2015 season, which is consistent with vaccine-driven selection within the H3N2 subtype. Overall, we find more support for vaccine-driven selection when large differences in vaccine effectiveness suggest a strong effect size. Variation in surveillance practices across countries could obscure signals of selection, especially when strain-specific differences in vaccine effectiveness are small. Further examination of the influenza vaccine’s evolutionary effects would benefit from improvements in epidemiological surveillance and reporting.

Cross-species transmission (CST) has led to many devastating epidemics, but is still a poorly understood phenomenon. HIV-1 and HIV-2 (human immunodeficiency virus 1 and 2), which have collectively caused over 35 million deaths, are the result of multiple CSTs from chimpanzees, gorillas, and sooty mangabeys. While the immediate history of HIV is known, there are over 45 lentiviruses that infect specific species of primates, and patterns of host switching are not well characterized. We thus took a phylogenetic approach to better understand the natural history of SIV recombination and CST. We modeled host species as a discrete character trait on the viral phylogeny and inferred historical host switches and the pairwise transmission rates between each pair of 24 primate hosts. We identify 14 novel, well-supported, ancient cross-species transmission events. We also find that lentiviral lineages vary widely in their ability to infect new host species: SIVcol (from colobus monkeys) is evolutionarily isolated, while SIVagms (from African green monkeys) frequently move between host subspecies. We also examine the origins of SIVcpz (the predecessor of HIV-1) in greater detail than previous studies, and find that there are still large portions of the genome with unknown origins. Observed patterns of CST are likely driven by a combination of ecological circumstance and innate immune factors. Virus cross-species transmissions (CSTs) can cause devastating epidemics, including EBOV, MERS, and HIV. HIV is actually one of over 45 similar viruses, called SIVs, which infect primates; the emergence of HIV as a human pathogen was the result of multiple CSTs, and yet it is not understood how commonly these viruses switch hosts. We used publicly available genetic sequence data to reconstruct the evolutionary history of these viruses. We found evidence for at least 13 instances of recombination between lineages of SIV, and identified at least 14 ancient cross-species transmission events where an SIV switched hosts. These results provide important ecological context to the HIV pandemic, suggesting that while SIV CST is relatively rare over evolutionary time, it is an important factor that has shaped SIV biology and evolution.

Cross-species transmission (CST) has led to many devastating epidemics, but is still a poorly understood phenomenon. HIV-1 and HIV-2 (human immunodeficiency virus 1 and 2), which have collectively caused over 35 million deaths, are the result of multiple CSTs from chimpanzees, gorillas, and sooty mangabeys. While the immediate history of HIV is known, there are over 45 lentiviruses that infect specific species of primates, and patterns of host switching are not well characterized. We thus took a phylogenetic approach to better understand the natural history of SIV recombination and CST. We modeled host species as a discrete character trait on the viral phylogeny and inferred historical host switches and the pairwise transmission rates between each pair of 24 primate hosts. We identify 14 novel, well-supported, ancient cross-species transmission events. We also find that lentiviral lineages vary widely in their ability to infect new host species: SIV col (from colobus monkeys) is evolutionarily isolated, while SIV agm s (from African green monkeys) frequently move between host subspecies. We also examine the origins of SIV cpz (the predecessor of HIV-1) in greater detail than previous studies, and find that there are still large portions of the genome with unknown origins. Observed patterns of CST are likely driven by a combination of ecological circumstance and innate immune factors.