An official website of the United States government
Abstract Exposure to pathogens throughout a lifetime influences immunity and organ function. Here, we explore how the systemic host-response to bacterial urinary tract infection (UTI) induces tissue-specific alterations to the mammary gland. Utilizing a combination of histological tissue analysis, single cell transcriptomics, and flow cytometry, we identify that mammary tissue from UTI-bearing mice displays collagen deposition, enlarged ductal structures, ductal hyperplasia with atypical epithelial transcriptomes and altered immune composition. Bacterial cells are absent in the mammary tissue and blood of UTI-bearing mice, therefore, alterations to the distal mammary tissue are mediated by the systemic host response to local infection. Furthermore, broad spectrum antibiotic treatment resolves the infection and restores mammary cellular and tissue homeostasis. Systemically, unresolved UTI correlates with increased plasma levels of the metalloproteinase inhibitor, TIMP1, which controls extracellular matrix remodeling and neutrophil function. Treatment of nulliparous and post-lactation UTI-bearing female mice with a TIMP1 neutralizing antibody, restores mammary tissue normal homeostasis, thus providing evidence for a link between the systemic host response during UTI and mammary gland alterations.
more »
« less
Phenotyping Immune Cells in Tumor and Healthy Tissue Using Flow Cytometry Data
We present an automated pipeline capable of distinguishing the phenotypes of myeloid-derived suppressor cells (MDSC) in healthy and tumor-bearing tissues in mice using flow cytometry data. In contrast to earlier work where samples are analyzed individually, we analyze all samples from each tissue collectively using a representative template for it. We demonstrate with 43 flow cytometry samples collected from three tissues, naive bone-marrow, spleens of tumor-bearing mice, and intra-peritoneal tumor, that a set of templates serves as a better classifier than popular machine learning approaches including support vector machines and neural networks. Our "interpretable machine learning" approach goes beyond classification and identifies distinctive phenotypes associated with each tissue, information that is clinically useful. Hence the pipeline presented here leads to better understanding of the maturation and differentiation of MDSCs using high-throughput data.
more »
« less
- Award ID(s):
- 1637534
- PAR ID:
- 10109989
- Date Published:
- Journal Name:
- BCB '18: Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Tumor stiffness has been associated with malignancy and increased risk for metastasis. Extensive research has been done investigating breast cancer cell lines’ responsiveness to surfaces of varying rigidities as well as examining the biophysical properties of breast cancer tumor samples. However, there is a critical gap regarding the relationship between cells’ mechanosensitivity in conjunction to biophysical properties of their extracellular matrix environment. To explore this relationship, we will analyze single-cell mechanosensitivity in comparison to tumor rigidity via shearwave ultrasound elastogrophy (SWE). Given the putative affiliation, we hypothesize that cells expressing invasive mechanosensitivity profiles will correlate with stiffer tumor regions. Using collagen gels containing different cell types, we derived biopsy-sized samples allowing us to optimize single-cell mechanosensitivity analysis. Cells were stained using different dyes corresponding to invasiveness. Subsequently, we analyzed their morphology. Morphological identification within organoid environments would allow for single-cell analysis without the aggression of tissue digestion, though preliminary results suggest high heterogeneity may not allow for confident cell identification solely on morphology. Thus, inquisition into cell viability and integrity was explored by analyzing the effects of tissue digestion with HyQtase on single-cells. Cell count and live-dead stain via flow cytometry allowed for analysis of single-cell viability. Lastly, cell integrity was evaluated by a 2D adhesion assay of isolated cells. The live/dead stain revealed that digestion resulted in isolation of approximately 10% of the original 500,000 cell population with 90–97% of the isolated population being live-cells (invasive and non-invasive respectively). Furthermore, the adhesion assay showed that these isolated single cells retained the ability to adhere to new surfaces, with no difference between the invasive and non-invasive cell types. These results show that cells are able to retain mechanosensitive properties following enzymatic digestion. However, they also suggest our digestion procedure is not aggressive enough to isolate invasive subpopulations that are more strongly imbedded in the original tissues. Development of these novel techniques will allow for accurate and confident analysis of precious human biopsy samples. Insight into the relationship between single-cell mechanosensitivity and tumor biophysical properties could elucidate pathways for metastasis inhibition and prevention.more » « less
-
INTRODUCTION Diverse phenotypes, including large brains relative to body size, group living, and vocal learning ability, have evolved multiple times throughout mammalian history. These shared phenotypes may have arisen repeatedly by means of common mechanisms discernible through genome comparisons. RATIONALE Protein-coding sequence differences have failed to fully explain the evolution of multiple mammalian phenotypes. This suggests that these phenotypes have evolved at least in part through changes in gene expression, meaning that their differences across species may be caused by differences in genome sequence at enhancer regions that control gene expression in specific tissues and cell types. Yet the enhancers involved in phenotype evolution are largely unknown. Sequence conservation–based approaches for identifying such enhancers are limited because enhancer activity can be conserved even when the individual nucleotides within the sequence are poorly conserved. This is due to an overwhelming number of cases where nucleotides turn over at a high rate, but a similar combination of transcription factor binding sites and other sequence features can be maintained across millions of years of evolution, allowing the function of the enhancer to be conserved in a particular cell type or tissue. Experimentally measuring the function of orthologous enhancers across dozens of species is currently infeasible, but new machine learning methods make it possible to make reliable sequence-based predictions of enhancer function across species in specific tissues and cell types. RESULTS To overcome the limits of studying individual nucleotides, we developed the Tissue-Aware Conservation Inference Toolkit (TACIT). Rather than measuring the extent to which individual nucleotides are conserved across a region, TACIT uses machine learning to test whether the function of a given part of the genome is likely to be conserved. More specifically, convolutional neural networks learn the tissue- or cell type–specific regulatory code connecting genome sequence to enhancer activity using candidate enhancers identified from only a few species. This approach allows us to accurately associate differences between species in tissue or cell type–specific enhancer activity with genome sequence differences at enhancer orthologs. We then connect these predictions of enhancer function to phenotypes across hundreds of mammals in a way that accounts for species’ phylogenetic relatedness. We applied TACIT to identify candidate enhancers from motor cortex and parvalbumin neuron open chromatin data that are associated with brain size relative to body size, solitary living, and vocal learning across 222 mammals. Our results include the identification of multiple candidate enhancers associated with brain size relative to body size, several of which are located in linear or three-dimensional proximity to genes whose protein-coding mutations have been implicated in microcephaly or macrocephaly in humans. We also identified candidate enhancers associated with the evolution of solitary living near a gene implicated in separation anxiety and other enhancers associated with the evolution of vocal learning ability. We obtained distinct results for bulk motor cortex and parvalbumin neurons, demonstrating the value in applying TACIT to both bulk tissue and specific minority cell type populations. To facilitate future analyses of our results and applications of TACIT, we released predicted enhancer activity of >400,000 candidate enhancers in each of 222 mammals and their associations with the phenotypes we investigated. CONCLUSION TACIT leverages predicted enhancer activity conservation rather than nucleotide-level conservation to connect genetic sequence differences between species to phenotypes across large numbers of mammals. TACIT can be applied to any phenotype with enhancer activity data available from at least a few species in a relevant tissue or cell type and a whole-genome alignment available across dozens of species with substantial phenotypic variation. Although we developed TACIT for transcriptional enhancers, it could also be applied to genomic regions involved in other components of gene regulation, such as promoters and splicing enhancers and silencers. As the number of sequenced genomes grows, machine learning approaches such as TACIT have the potential to help make sense of how conservation of, or changes in, subtle genome patterns can help explain phenotype evolution. Tissue-Aware Conservation Inference Toolkit (TACIT) associates genetic differences between species with phenotypes. TACIT works by generating open chromatin data from a few species in a tissue related to a phenotype, using the sequences underlying open and closed chromatin regions to train a machine learning model for predicting tissue-specific open chromatin and associating open chromatin predictions across dozens of mammals with the phenotype. [Species silhouettes are from PhyloPic]more » « less
-
null (Ed.)Abstract Development of an assay to predict response to chemotherapy has remained an elusive goal in cancer research. We report a phenotypic chemosensitivity assay for epithelial ovarian cancer based on Doppler spectroscopy of infrared light scattered from intracellular motions in living three-dimensional tumor biopsy tissue measured in vitro. The study analyzed biospecimens from 20 human patients with epithelial ovarian cancer. Matched primary and metastatic tumor tissues were collected for 3 patients, and an additional 3 patients provided only metastatic tissues. Doppler fluctuation spectra were obtained using full-field optical coherence tomography through off-axis digital holography. Frequencies in the range from 10 mHz to 10 Hz are sensitive to changes in intracellular dynamics caused by platinum-based chemotherapy. Metastatic tumor tissues were found to display a biodynamic phenotype that was similar to primary tissue from patients who had poor clinical outcomes. The biodynamic phenotypic profile correctly classified 90% [88–91% c.i.] of the patients when the metastatic samples were characterized as having a chemoresistant phenotype. This work suggests that Doppler profiling of tissue response to chemotherapy has the potential to predict patient clinical outcomes based on primary, but not metastatic, tumor tissue.more » « less
-
Abstract To understand phenotypic variations and key factors which affect disease susceptibility of complex traits, it is important to decipher cell‐type tissue compositions. To study cellular compositions of bulk tissue samples, one can evaluate cellular abundances and cell‐type‐specific gene expression patterns from the tissue transcriptome profiles. We develop both fixed and mixed models to reconstruct cellular expression fractions for bulk‐profiled samples by using reference single‐cell (sc) RNA‐sequencing (RNA‐seq) reference data. In benchmark evaluations of estimating cellular expression fractions, the mixed‐effect models provide similar results as an elegant machine learning algorithm named cell‐type identification by estimating relative subsets of RNA transcripts (CIBERSORTx), which is a well‐known and reliable procedure to reconstruct cell‐type abundances and cell‐type‐specific gene expression profiles. In real data analysis, the mixed‐effect models outperform or perform similarly as CIBERSORTx. The mixed models perform better than the fixed models in both benchmark evaluations and data analysis. In simulation studies, we show that if the heterogeneity exists in scRNA‐seq data, it is better to use mixed models with heterogeneous mean and variance–covariance. As a byproduct, the mixed models provide fractions of covariance between subject‐specific gene expression and cell types to measure their correlations. The proposed mixed models provide a complementary tool to dissect bulk tissues using scRNA‐seq data.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
