Biology subjects

Blattman, S. B.

Publications and source records attributed to Blattman, S. B..

3 recordsLinked to original sources

bioRxiv2026

Scalable genotyping in fixed transcriptomes resolves clonal heterogeneity via single-cell sequencing

Despite the promise of single-cell transcriptomics for understanding cell states in heterogeneous populations, widely used platforms have limited ability to link transcriptional states to somatic mutations within the same cells. Here, we introduce Genotyping in Fixed Transcriptomes (GIFT) for the simultaneous detection of large numbers of targeted genetic variants with whole transcriptome profiles in single cells. The core innovation of GIFT is a rationally designed gapfilling reaction between adjacent single-stranded DNA (ssDNA) probes that barcodes native transcript sequence to enable highly-specific targeted mutation detection. GIFT achieves greater than 99% genotyping accuracy and flexible capture of hundreds of mutations per cell, including in formalin-fixed, paraffin-embedded (FFPE) tissue, enabling clonal lineage tracing in heterogeneous settings. We demonstrate the unique scalability of GIFT by profiling more than 700,000 cells from 35 donors with myeloproliferative neoplasms (MPN), revealing mutation-dependent hematopoietic responses to systemic inflammation associated with the characteristic JAK2V617 mutation, including an allelic dose gradient of interferon-associated transcriptional programs and priming of hematopoietic stem cells that develop into divergent disease states. The technical advantages of GIFT enable direct resolution of genotype-to-phenotype relationships via clonal tracing with comprehensive cell-state measurements at single-cell resolution.

genomics↗

bioRxiv2024

The genetic landscape of antibiotic sensitivity in Staphylococcus aureus

A comprehensive genetic landscape of antibiotic sensitivity in Staphylococcus aureus is lacking. Using ultra-dense CRISPR-interference libraries, we systematically quantified global gene fitness across ten antibiotics and uncovered hundreds of significant antibiotic-gene interactions. Essential genes dominated these interactions, a finding not revealed by transposon-based studies. Processes most vulnerable to transcriptional repression under antibiotic conditions included cell wall synthesis/cell division (CC), DNA replication/DNA recombination (DD), coenzyme A biosynthesis, and riboflavin metabolism. Network and genetic analyses further revealed novel synergistic genetic interactions (GIs) within these processes, including an extensive CC-DD subnetwork. Only a subset of CC-DD synergies was dependent on the cell division inhibitor SosA. Informed by these GIs, we identified multiple drug-drug combinations with potent synergistic activity against multidrug-resistant S. aureus. Our detailed profiling of drug-gene, gene-gene, and drug-drug interactions reveals novel functional relationships among essential genes and defines a vulnerability landscape to guide new drug target discovery and effective combination therapies.

systems biology↗

bioRxiv2019

Prokaryotic Single-Cell RNA Sequencing by In Situ Combinatorial Indexing

Despite longstanding appreciation of gene expression heterogeneity in isogenic bacterial populations, affordable and scalable technologies for studying single bacterial cells have been limited. While single-cell RNA sequencing (scRNA-seq) has revolutionized studies of transcriptional heterogeneity in diverse eukaryotic systems, application of scRNA-seq to prokaryotes has been hindered by their extremely low mRNA abundance, lack of mRNA polyadenylation, and thick cell walls. Here, we present Prokaryotic Expression-profiling by Tagging RNA In Situ and sequencing (PETRI-seq), a low-cost, high-throughput, prokaryotic scRNA-seq pipeline that overcomes these technical obstacles. PETRI-seq uses in situ combinatorial indexing to barcode transcripts from tens of thousands of cells in a single experiment. PETRI-seq captures single cell transcriptomes of Gram-negative and Gram-positive bacteria with high purity and low bias, with median capture rates >200 mRNAs/cell for exponentially growing E. coli. These characteristics enable robust discrimination of cell-states corresponding to different phases of growth. When applied to wild-type S. aureus, PETRI-seq revealed a rare sub-population of cells undergoing prophage induction. We anticipate broad utility of PETRI-seq in defining single-cell states and their dynamics in complex microbial communities.

systems biology↗