Single-cell RNA sequencing of liver fine-needle aspirates captures immune diversity in the blood and liver in chronic hepatitis B patients.

BACKGROUND AND AIMS: HBV infection is restricted to the liver, where it drives exhaustion of virus-specific T and B cells and pathogenesis through dysregulation of intrahepatic immunity. Our understanding of liver-specific events related to viral control and liver damage has relied almost solely on animal models, and we lack useable peripheral biomarkers to quantify intrahepatic immune activation beyond cytokine measurement. Our objective was to overcome the practical obstacles of liver sampling using fine-needle aspiration and develop an optimized workflow to comprehensively compare the blood and liver compartments within patients with chronic hepatitis B using single-cell RNA sequencing. APPROACH AND RESULTS: We developed a workflow that enabled multi-site international studies and centralized single-cell RNA sequencing. Blood and liver fine-needle aspirations were collected, and cellular and molecular captures were compared between the Seq-Well S 3 picowell-based and the 10× Chromium reverse-emulsion droplet-based single-cell RNA sequencing technologies. Both technologies captured the cellular diversity of the liver, but Seq-Well S 3 effectively captured neutrophils, which were absent in the 10× dataset. CD8 T cells and neutrophils displayed distinct transcriptional profiles between blood and liver. In addition, liver fine-needle aspirations captured a heterogeneous liver macrophage population. Comparison between untreated patients with chronic hepatitis B and patients treated with nucleoside analogs showed that myeloid cells were highly sensitive to environmental changes while lymphocytes displayed minimal differences. CONCLUSIONS: The ability to electively sample and intensively profile the immune landscape of the liver, and generate high-resolution data, will enable multi-site clinical studies to identify biomarkers for intrahepatic immune activity in HBV and beyond.

Live cell tagging tracking and isolation for spatial transcriptomics using photoactivatable cell dyes.

A cell's phenotype and function are influenced by dynamic interactions with its microenvironment. To examine cellular spatiotemporal activity, we developed SPACECAT-Spatially PhotoActivatable Color Encoded Cell Address Tags-to annotate, track, and isolate cells while preserving viability. In SPACECAT, samples are stained with photocaged fluorescent molecules, and cells are labeled by uncaging those molecules with user-patterned near-UV light. SPACECAT offers single-cell precision and temporal stability across diverse cell and tissue types. Illustratively, we target crypt-like regions in patient-derived intestinal organoids to enrich for stem-like and actively mitotic cells, matching literature expectations. Moreover, we apply SPACECAT to ex vivo tissue sections from four healthy organs and an autochthonous lung tumor model. Lastly, we provide a computational framework to identify spatially-biased transcriptome patterns and enriched phenotypes. This minimally perturbative and broadly applicable method links cellular spatiotemporal and/or behavioral phenotypes with diverse downstream assays, enabling insights into the connections between tissue microenvironments and (dys)function.

Exploring the Influence of Domain Architecture on the Catalytic Function of Diterpene Synthases.

Terpenoid synthases catalyze isoprenoid cyclization reactions underlying the generation of more than 80,000 natural products. Such dramatic chemodiversity belies the fact that these enzymes generally consist of only three domain folds designated as α, ß, and γ. Catalysis by class I terpenoid synthases occurs exclusively in the α domain, which is found with α, αα, αß, and αßγ domain architectures. Here, we explore the influence of domain architecture on catalysis by taxadiene synthase from Taxus brevifolia (TbTS, αßγ), fusicoccadiene synthase from Phomopsis amygdali (PaFS, (αα)6), and ophiobolin F synthase from Aspergillus clavatus (AcOS, αα). We show that the cyclization fidelity and catalytic efficiency of the α domain of TbTS are severely compromised by deletion of the ßγ domains; however, retention of the ß domain preserves significant cyclization fidelity. In PaFS, we previously demonstrated that one α domain similarly influences catalysis by the other α domain [ Chen , M. , Chou , W. K. W. , Toyomasu , T. , Cane , D. E. , and Christianson , D. W. ( 2016 ) ACS Chem. Biol. 11 , 889 - 899 ]. Here, we show that the hexameric quaternary structure of PaFS enables cluster channeling. We also show that the α domains of PaFS and AcOS can be swapped so as to make functional chimeric αα synthases. Notably, both cyclization fidelity and catalytic efficiency are altered in all chimeric synthases. Twelve newly formed and uncharacterized C20 diterpene products and three C25 sesterterpene products are generated by these chimeras. Thus, engineered αßγ and αα terpenoid cyclases promise to generate chemodiversity in the greater family of terpenoid natural products.

Multiplexed, targeted profiling of single-cell proteomes and transcriptomes in a single reaction.

We present a scalable, integrated strategy for coupled protein and RNA detection from single cells. Our approach leverages the DNA polymerase activity of reverse transcriptase to simultaneously perform proximity extension assays and complementary DNA synthesis in the same reaction. Using the Fluidigm C1™ system, we profile the transcriptomic and proteomic response of a human breast adenocarcinoma cell line to a chemical perturbation, benchmarking against in situ hybridizations and immunofluorescence staining, as well as recombinant proteins, ERCC Spike-Ins, and population lysate dilutions. Through supervised and unsupervised analyses, we demonstrate synergies enabled by simultaneous measurement of single-cell protein and RNA abundances. Collectively, our generalizable approach highlights the potential for molecular metadata to inform highly-multiplexed single-cell analyses.

Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq.

To explore the distinct genotypic and phenotypic states of melanoma tumors, we applied single-cell RNA sequencing (RNA-seq) to 4645 single cells isolated from 19 patients, profiling malignant, immune, stromal, and endothelial cells. Malignant cells within the same tumor displayed transcriptional heterogeneity associated with the cell cycle, spatial context, and a drug-resistance program. In particular, all tumors harbored malignant cells from two distinct transcriptional cell states, such that tumors characterized by high levels of the MITF transcription factor also contained cells with low MITF and elevated levels of the AXL kinase. Single-cell analyses suggested distinct tumor microenvironmental patterns, including cell-to-cell interactions. Analysis of tumor-infiltrating T cells revealed exhaustion programs, their connection to T cell activation and clonal expansion, and their variability across patients. Overall, we begin to unravel the cellular ecosystem of tumors and how single-cell genomics offers insights with implications for both targeted and immune therapies.

A microfluidic platform enabling single-cell RNA-seq of multigenerational lineages.

We introduce a microfluidic platform that enables off-chip single-cell RNA-seq after multi-generational lineage tracking under controlled culture conditions. We use this platform to generate whole-transcriptome profiles of primary, activated murine CD8+ T-cell and lymphocytic leukemia cell line lineages. Here we report that both cell types have greater intra- than inter-lineage transcriptional similarity. For CD8+ T-cells, genes with functional annotation relating to lymphocyte differentiation and function--including Granzyme B--are enriched among the genes that demonstrate greater intra-lineage expression level similarity. Analysis of gene expression covariance with matched measurements of time since division reveals cell type-specific transcriptional signatures that correspond with cell cycle progression. We believe that the ability to directly measure the effects of lineage and cell cycle-dependent transcriptional profiles of single cells will be broadly useful to fields where heterogeneous populations of cells display distinct clonal trajectories, including immunology, cancer, and developmental biology.