Biology subjects

Butte, A. J.

Publications and source records attributed to Butte, A. J..

6 recordsLinked to original sources

bioRxiv2018

Single-cell RNA-seq reveals profibrotic macrophages in lung fibrosis

Myeloid cells localize to peripheral tissues in a wide range of pathologic contexts. However, appreciation of distinct myeloid subtypes has been limited by the signal averaging inherent to bulk sequencing approaches. Here we applied single-cell RNA sequencing (scRNA-seq) to map cellular heterogeneity in lung fibrosis induced by bleomycin injury in mice. We first developed a computational framework that enables unbiased, granular cell-type annotation of scRNA-seq. This approach identified a macrophage subpopulation that was specific to injured lung and notable for high expression of Cx3cr1+ and MHCII genes. We found that these macrophages, which bear a gene expression profile consistent with monocytic origin, progressively acquire alveolar macrophage identity and localize to sites of fibroblast accumulation. Probing their functional role, in vitro studies showed a trophic effect of these cells on fibroblast activation, and ablation of Cx3cr1-expressing cells suppressed fibrosis in vivo. We also found by gene set analysis and immunofluorescence that markers of these macrophages were upregulated in samples from patients with lung fibrosis compared with healthy controls. Taken together, our results uncover a specific pathologic subgroup of macrophages with markers that could enable their therapeutic targeting for fibrosis.

immunology↗

bioRxiv2018

Tumor cell-adipocyte gap junctions activate lipolysis and are essential for breast tumorigenesis

A pro-tumorigenic role for adipocytes has been identified in breast cancer, and reliance on fatty acid catabolism found in aggressive tumors. The molecular mechanisms by which tumor cells coopt neighboring adipocytes, however, remain elusive. Here, we describe a direct interaction linking tumorigenesis to adjacent adipocytes. We examine breast tumors and their normal adjacent tissue from several patient cohorts, patient-derived xenografts and mouse models, and find that lipolysis and lipolytic signaling are activated in neighboring adipose tissue. We find that functional gap junctions form between breast cancer cells and adipocytes. As a result, cAMP is transferred from breast cancer cells to adipocytes and activates lipolysis in a gap junction-dependent manner. We identify connexin 31 (GJB3), which promotes receptor triple negative breast cancer growth and activation of lipolysis in vivo. Thus, direct tumor cell-adipocyte interaction contributes to tumorigenesis and may serve as a new therapeutic target in breast cancer. One sentence summaryGap junctions between breast cancer cells and adipocytes transfer cAMP and activate lipolysis in the breast tumor microenvironment to support growth.

cancer biology↗

bioRxiv2017

Creating ethnicity-specific reference intervals for lab tests from EHR data

The results of clinical lab tests are an essential component of medical decision-making. To guide interpretation, test results are returned with reference intervals defined by the range in which 95% of values occur in healthy individuals. Clinical laboratories often set their own reference intervals to accommodate local population and instruments variations. This approach is costly and can be biased. We describe a novel data-driven method for using electronic health record data to extract healthy patients information to define reference intervals. We found that the distributions of many clinical lab tests differ among self-identified racial and ethnic groups (SIREs) in healthy patients. Finally, we derived SIRE-specific reference intervals and provide evidence that these intervals have clinical prognostic value. Specifically, we show that for two lab tests, serum creatinine level and hemoglobin A1C, SIRE-specific reference intervals are more predictive for need for dialysis and development type 2 diabetes than existing reference intervals.\n\nOne Sentence SummaryA novel method for defining population-specific reference intervals of common clinical laboratory tests from electronical health records has better prognostic value than existing reference intervals.

bioinformatics↗

bioRxiv2017

The 10,000 Immunomes Project: A resource for human immunology

New immunological assays now enable rich measurements of human immune function, but difficulty attaining enough measurements across sufficiently large and diverse cohorts has hindered describing normal human immune physiology on a large scale. Here we present the 10,000 Immunomes Project (10KIP), a diverse human immunology reference derived from over 44,000 individuals across 242 studies from ImmPort, a publicly available resource of raw immunology study data and protocols. We carefully curated datasets, aggregating subjects from healthy/control arms and harmonizing data across studies. We demonstrate 10KIPs utility by describing variations in serum cytokines and leukocytes by age, race, and sex; defining a baseline cell-cytokine network; and using 10KIP as a common control to describe immunologic changes in pregnancy. Subject-level data is available for interactive visualization and download at http://10kImmunomes.org/. We believe 10KIP can serve as a common control cohort and will accelerate hypothesis generation by clinical and basic immunologists across diverse populations.\n\nOne Sentence SummaryAn open online resource of human immunology data from more than 10,000 normal subjects including interactive data visualization and download enables a new look at immune system differences across age and sex, rapid hypothesis generation, and creation of custom control cohorts.

immunology↗

bioRxiv2017

Meta-analysis of Cytometry Data Reveals Racial Differences in Immune Cells

While meta-analysis has demonstrated increased statistical power and more robust estimations in studies, the application of this commonly accepted methodology to cytometry data has been challenging. Different cytometry studies often involve diverse sets of markers. Moreover, the detected values of the same marker are inconsistent between studies due to different experimental designs and cytometer configurations. As a result, the cell subsets identified by existing auto-gating methods cannot be directly compared across studies. We developed MetaCyto for automated meta-analysis of both flow and mass cytometry (CyTOF) data. By combining clustering methods with a silhouette scanning method, MetaCyto is able to identify commonly labeled cell subsets across studies, thus enabling meta-analysis. Applying MetaCyto across a set of 10 heterogeneous cytometry studies totaling 2926 samples enabled us to identify multiple cell populations exhibiting differences in abundance between White and Asian adults. Software is released to the public through GitHub (github.com/hzc363/MetaCyto).

bioinformatics↗

bioRxiv2017

xCell: Digitally portraying the tissue cellular heterogeneity landscape

Tissues are complex milieu consisting of numerous cell-types. Numerous recent methods attempt to enumerate cell subsets from transcriptomes. However, available method used limited source for training and displayed only partial portrayal of the full cellular landscape. Here we present xCell, a novel gene-signature based method for inferring 64 immune and stroma cell-types. We harmonized 1,822 pure human cell-types transcriptomes from various sources, employed curve fitting approach for linear comparison of cell-types, and introduced a novel spillover compensation technique for separating between cell-types. Using extensive in silico analyses and comparison to cytometry immunophenotyping we show that xCell outperforms other methods: http://xCell.ucsf.edu/.

bioinformatics↗