Clonal Decomposition and DNA Replication States Defined by Scaled Single-Cell Genome Sequencing.

Accurate measurement of clonal genotypes, mutational processes, and replication states from individual tumor-cell genomes will facilitate improved understanding of tumor evolution. We have developed DLP+, a scalable single-cell whole-genome sequencing platform implemented using commodity instruments, image-based object recognition, and open source computational methods. Using DLP+, we have generated a resource of 51,926 single-cell genomes and matched cell images from diverse cell types including cell lines, xenografts, and diagnostic samples with limited material. From this resource we have defined variation in mitotic mis-segregation rates across tissue types and genotypes. Analysis of matched genomic and image measurements revealed correlations between cellular morphology and genome ploidy states. Aggregation of cells sharing copy number profiles allowed for calculation of single-nucleotide resolution clonal genotypes and inference of clonal phylogenies and avoided the limitations of bulk deconvolution. Finally, joint analysis over the above features defined clone-specific chromosomal aneuploidy in polyclonal populations.

Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling.

Single-cell RNA sequencing has enabled the decomposition of complex tissues into functionally distinct cell types. Often, investigators wish to assign cells to cell types through unsupervised clustering followed by manual annotation or via 'mapping' to existing data. However, manual interpretation scales poorly to large datasets, mapping approaches require purified or pre-annotated data and both are prone to batch effects. To overcome these issues, we present CellAssign, a probabilistic model that leverages prior knowledge of cell-type marker genes to annotate single-cell RNA sequencing data into predefined or de novo cell types. CellAssign automates the process of assigning cells in a highly scalable manner across large datasets while controlling for batch and sample effects. We demonstrate the advantages of CellAssign through extensive simulations and analysis of tumor microenvironment composition in high-grade serous ovarian cancer and follicular lymphoma.

Active management of atrial fibrillation or flutter in emergency department patients with renal impairment is associated with a higher risk of adverse events and treatment failure.

OBJECTIVE: Atrial fibrillation or flutter (AFF) patients with renal impairment have poor long-term prognosis, but their emergency department (ED) management has not been described. We investigated the association of renal impairment upon outcomes after rate or rhythm control (RRC) including ED-based adverse events (AE) and treatment failure. METHODS: This cohort study used an electrocardiogram database from two urban centres to identify consecutive AFF patients and reviewed charts to obtain comorbidities, ED management, including RRC, prespecified AE, and treatment failure. Patients were dichotomized into a normal estimated glomerular filtration rate (eGFR) > 60 mL/min/1.73 m2) or impaired renal function ("low eGFR"). Primary and secondary outcomes were prespecified AEs and treatment failure, respectively. We calculated 1) adjusted excess AE risk for patients with decreased renal function receiving RRC; and 2) adjusted odds ratio of RRC treatment failure. RESULTS: Of 1,112 consecutive ED AFF patients, 412 (37.0%) had a low eGFR. Crude AE rates for RRC were 27/238 (11.3%) for patients with normal renal function and 26/103 (25.2%) for patients with low eGFR. For patients with low eGFR receiving RRC, adjusted excess AE risk was 13.7%. (95% CI 1.7 to 25.1%). For patients with low eGFR, adjusted odds ratio for RRC failure was 3.07. (95% CI 1.74 to 5.43) CONCLUSIONS: In this cohort of ED AFF patients receiving RRC, those with low eGFR had significantly increased adjusted excess risk of AE compared with patients with normal renal function. Odds of treatment failure were also significantly increased.

Dissociation of solid tumor tissues with cold active protease for single-cell RNA-seq minimizes conserved collagenase-associated stress responses.

BACKGROUND: Single-cell RNA sequencing (scRNA-seq) is a powerful tool for studying complex biological systems, such as tumor heterogeneity and tissue microenvironments. However, the sources of technical and biological variation in primary solid tumor tissues and patient-derived mouse xenografts for scRNA-seq are not well understood. RESULTS: We use low temperature (6 °C) protease and collagenase (37 °C) to identify the transcriptional signatures associated with tissue dissociation across a diverse scRNA-seq dataset comprising 155,165 cells from patient cancer tissues, patient-derived breast cancer xenografts, and cancer cell lines. We observe substantial variation in standard quality control metrics of cell viability across conditions and tissues. From the contrast between tissue protease dissociation at 37 °C or 6 °C, we observe that collagenase digestion results in a stress response. We derive a core gene set of 512 heat shock and stress response genes, including FOS and JUN, induced by collagenase (37 °C), which are minimized by dissociation with a cold active protease (6 °C). While induction of these genes was highly conserved across all cell types, cell type-specific responses to collagenase digestion were observed in patient tissues. CONCLUSIONS: The method and conditions of tumor dissociation influence cell yield and transcriptome state and are both tissue- and cell-type dependent. Interpretation of stress pathway expression differences in cancer single-cell studies, including components of surface immune recognition such as MHC class I, may be especially confounded. We define a core set of 512 genes that can assist with the identification of such effects in dissociated scRNA-seq experiments.