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Single-cell transcriptomics reveals gene expression heterogeneity but suffers from stochastic dropout and characteristic bimodal expression distributions in which expression is either strongly non-zero or non-detectable. We propose a two-part, generalized linear model for such bimodal data that parameterizes both of these features. We argue that the cellular detection rate, the fraction of genes expressed in a cell, should be adjusted for as a source of nuisance variation. Our model provides gene set enrichment analysis tailored to single-cell data. It provides insights into how networks of co-expressed genes evolve across an experimental treatment. MAST is available at https://github.com/RGLab/MAST .

Preterm birth increases the risk for pulmonary hypertension and heart failure in adulthood. Oxygen therapy can damage the immature cardiopulmonary system and may be partially responsible for the cardiovascular disease in adults born preterm. We previously showed that exposing newborn mice to hyperoxia causes pulmonary hypertension by 1 year of age that is preceded by a poorly understood loss of pulmonary vein cardiomyocyte proliferation. We now show that hyperoxia also reduces cardiomyocyte proliferation and survival in the left atrium and causes diastolic heart failure by disrupting its filling of the left ventricle. Transcriptomic profiling showed that neonatal hyperoxia permanently suppressed fatty acid synthase ( Fasn ), stearoyl-CoA desaturase 1 ( Scd1 ), and other fatty acid synthesis genes in the atria of mice, the HL-1 line of mouse atrial cardiomyocytes, and left atrial tissue explanted from human infants. Suppressing Fasn or Scd1 reduced HL-1 cell proliferation and increased cell death, while overexpressing these genes maintained their expansion in hyperoxia, suggesting that oxygen directly inhibits atrial cardiomyocyte proliferation and survival by repressing Fasn and Scd1 . Pharmacologic interventions that restore Fasn , Scd1 , and other fatty acid synthesis genes in atrial cardiomyocytes may, thus, provide a way of ameliorating the adverse effects of supplemental oxygen on preterm infants.

Abstract Oxygen supplementation in preterm infants disrupts alveolar epithelial type 2 (AT2) cell proliferation through poorly understood mechanisms. Here, newborn mice are used to understand how hyperoxia stimulates an early aberrant wave of AT2 cell proliferation that occurs between Postnatal Days (PNDs) 0 and 4. RNA-sequencing analysis of AT2 cells isolated from PND4 mice revealed hyperoxia stimulates expression of mitochondrial-specific methylenetetrahydrofolate dehydrogenase 2 and other genes involved in mitochondrial one-carbon coupled folate metabolism and serine synthesis. The same genes are induced when AT2 cells normally proliferate on PND7 and when they proliferate in response to the mitogen fibroblast growth factor 7. However, hyperoxia selectively stimulated their expression via the stress-responsive activating transcription factor 4 (ATF4). Administration of the mitochondrial superoxide scavenger mitoTEMPO during hyperoxia suppressed ATF4 and thus early AT2 cell proliferation, but it had no effect on normative AT2 cell proliferation seen on PND7. Because ATF4 and methylenetetrahydrofolate dehydrogenase are detected in hyperplastic AT2 cells of preterm infant humans and baboons with bronchopulmonary dysplasia, dampening mitochondrial oxidative stress and ATF4 activation may provide new opportunities for controlling excess AT2 cell proliferation in neonatal lung disease.

Background Postnatal development of early life microbiota influences immunity, metabolism, neurodevelopment, and infant health. Microbiome development occurs at multiple body sites, with distinct community compositions and functions. Associations between microbiota at multiple sites represent an unexplored influence on the infant microbiome. Here, we examined co-occurrence patterns of gut and respiratory microbiota in pre- and full-term infants over the first year of life, a period critical to neonatal development. Results Gut and respiratory microbiota collected as longitudinal rectal, throat, and nasal samples from 38 pre-term and 44 full-term infants were first clustered into community state types (CSTs) on the basis of their compositional profiles. Multiple methods were used to relate the occurrence of CSTs to temporal microbiota development and measures of infant maturity, including gestational age (GA) at birth, week of life (WOL), and post-menstrual age (PMA). Manifestation of CSTs followed one of three patterns with respect to infant maturity: (1) chronological , with CST occurrence frequency solely a function of post-natal age (WOL), (2) idiosyncratic to maturity at birth , with the interval of CST occurrence dependent on infant post-natal age but the frequency of occurrence dependent on GA at birth, and (3) convergent , in which CSTs appear first in infants of greater maturity at birth, with occurrence frequency in pre-terms converging after a post-natal interval proportional to pre-maturity. The composition of CSTs was highly dissimilar between different body sites, but the CST of any one body site was highly predictive of the CSTs at other body sites. There were significant associations between the abundance of individual taxa at each body site and the CSTs of the other body sites, which persisted after stringent control for the non-linear effects of infant maturity. Canonical correlations exist between the microbiota composition at each pair of body sites, with the strongest correlations between proximal locations. Conclusion These findings suggest that early microbiota is shaped by neonatal innate and adaptive developmental responses. Temporal progression of CST occurrence is influenced by infant maturity at birth and post-natal age. Significant associations of microbiota across body sites reveal distal connections and coordinated development of the infant microbial ecosystem.

Advances in high-throughput, single cell gene expression are allowing interrogation of cell heterogeneity. However, there is concern that the cell cycle phase of a cell might bias characterizations of gene expression at the single-cell level. We assess the effect of cell cycle phase on gene expression in single cells by measuring 333 genes in 930 cells across three phases and three cell lines. We determine each cell's phase non-invasively without chemical arrest and use it as a covariate in tests of differential expression. We observe bi-modal gene expression, a previously-described phenomenon, wherein the expression of otherwise abundant genes is either strongly positive, or undetectable within individual cells. This bi-modality is likely both biologically and technically driven. Irrespective of its source, we show that it should be modeled to draw accurate inferences from single cell expression experiments. To this end, we propose a semi-continuous modeling framework based on the generalized linear model, and use it to characterize genes with consistent cell cycle effects across three cell lines. Our new computational framework improves the detection of previously characterized cell-cycle genes compared to approaches that do not account for the bi-modality of single-cell data. We use our semi-continuous modelling framework to estimate single cell gene co-expression networks. These networks suggest that in addition to having phase-dependent shifts in expression (when averaged over many cells), some, but not all, canonical cell cycle genes tend to be co-expressed in groups in single cells. We estimate the amount of single cell expression variability attributable to the cell cycle. We find that the cell cycle explains only 5%-17% of expression variability, suggesting that the cell cycle will not tend to be a large nuisance factor in analysis of the single cell transcriptome.

While animal model studies have extensively defined the mechanisms controlling cell diversity in the developing mammalian lung, there exists a significant knowledge gap with regards to late-stage human lung development. The NHLBI Molecular Atlas of Lung Development Program (LungMAP) seeks to fill this gap by creating a structural, cellular and molecular atlas of the human and mouse lung. Transcriptomic profiling at the single-cell level created a cellular atlas of newborn human lungs. Frozen single-cell isolates obtained from two newborn human lungs from the LungMAP Human Tissue Core Biorepository, were captured, and library preparation was completed on the Chromium 10X system. Data was analyzed in Seurat, and cellular annotation was performed using the ToppGene functional analysis tool. Transcriptional interrogation of 5500 newborn human lung cells identified distinct clusters representing multiple populations of epithelial, endothelial, fibroblasts, pericytes, smooth muscle, immune cells and their gene signatures. Computational integration of data from newborn human cells and with 32,000 cells from postnatal days 1 through 10 mouse lungs generated by the LungMAP Cincinnati Research Center facilitated the identification of distinct cellular lineages among all the major cell types. Integration of the newborn human and mouse cellular transcriptomes also demonstrated cell type-specific differences in maturation states of newborn human lung cells. Specifically, newborn human lung matrix fibroblasts could be separated into those representative of younger cells (n = 393), or older cells (n = 158). Cells with each molecular profile were spatially resolved within newborn human lung tissue. This is the first comprehensive molecular map of the cellular landscape of neonatal human lung, including biomarkers for cells at distinct states of maturity.

Recently, we profiled gene expression in 930 cells targeting canonical cell cycle genes (ranked genes) and genes without known cell cycle annotation (unranked genes) across three cell lines2. We estimated that cell cycle explained 17% of the generalized linear model deviance (analogous to ANOVA R2) in the typical ranked gene, and 5% in the typical unranked gene. On the basis of these results, we concluded that cell cycle did not cause substantial variability in single-cell gene expression.

In clinical and translational research, data science is often and fortuitously integrated with data collection. This contrasts to the typical position of data scientists in other settings, where they are isolated from data collectors. Because of this, effective use of data science techniques to resolve translational questions requires innovation in the organization and management of these data. We propose an operational framework that respects this important difference in how research teams are organized. To maximize the accuracy and speed of the clinical and translational data science enterprise under this framework, we define a set of eight best practices for data management. In our own work at the University of Rochester, we have strived to utilize these practices in a customized version of the open source LabKey platform for integrated data management and collaboration. We have applied this platform to cohorts that longitudinally track multidomain data from over 3000 subjects. We argue that this has made analytical datasets more readily available and lowered the bar to interdisciplinary collaboration, enabling a team-based data science that is unique to the clinical and translational setting.

Chromosomal abnormalities provide clinical utility in the diagnosis and treatment of hematologic malignancies, and may be predictive of malignant transformation in individuals without apparent clinical presentation of a hematologic cancer. In an effort to confirm previous reports of an association between clonal mosaicism and incident hematologic cancer, we applied the anomDetectBAF algorithm to call chromosomal anomalies in genotype data from previously conducted Genome Wide Association Studies (GWAS). The genotypes were initially collected from DNA derived from peripheral blood of 12,176 participants in the Group Health electronic Medical Records and Genomics study (eMERGE) and the Women's Health Initiative (WHI). We detected clonal mosaicism in 169 individuals (1.4%) and large clonal mosaic events (>2 mb) in 117 (1.0%) individuals. Though only 9.5% of clonal mosaic carriers had an incident diagnosis of hematologic cancer (multiple myeloma, myelodysplastic syndrome, lymphoma, or leukemia), the carriers had a 5.5-fold increased risk (95% CI: 3.3-9.3; p-value = 7.5×10(-11)) of developing these cancers subsequently. Carriers of large mosaic anomalies showed particularly pronounced risk of subsequent leukemia (HR = 19.2, 95% CI: 8.9-41.6; p-value = 7.3×10(-14)). Thus we independently confirm the association between detectable clonal mosaicism and hematologic cancer found previously in two recent publications.

The feasibility of using imperfectly phenotyped \"silver standard\" samples identified from electronic medical record diagnoses is considered in genetic association studies when these samples might be combined with an existing set of samples phenotyped with a gold standard technique. An analytic expression is derived for the power of a chi-square test of independence using either research-quality case/control samples alone, or augmented with silver standard data. The subset of the parameter space where inclusion of silver standard samples increases statistical power is identified. A case study of dementia subjects identified from electronic medical records from the Electronic Medical Records and Genomics (eMERGE) network, combined with subjects from two studies specifically targeting dementia, verifies these results.