Biology subjects

Mignardi, M.

Publications and source records attributed to Mignardi, M..

3 recordsLinked to original sources

bioRxiv2018

Modeling Spatial Correlation of Transcripts With Application to Developing Pancreas

Recently high-throughput image-based transcriptomic methods were developed and enabled researchers to spatially resolve gene expression variation at the molecular level for the first time. In this work, we develop a general analysis tool to quantitatively study the spatial correlations of gene expression in fixed tissue sections. As an illustration, we analyze the spatial distribution of single mRNA molecules measured by in situ sequencing on human fetal pancreas at three developmental time points 80, 87 and 117 days post-fertilization. We develop a density profile-based method to capture the spatial relationship between gene expression and other morphological features of the tissue sample such as position of nuclei and endocrine cells of the pancreas. In addition, we build a statistical model to characterize correlations in the spatial distribution of the expression level among different genes. This model enables us to infer the inhibitory and clustering effects throughout different time points. Our analysis framework is applicable to a wide variety of spatially-resolved transcriptomic data to derive biological insights.

bioinformatics↗

bioRxiv2017

Single-Cell RNAseq analysis of infiltrating neoplastic cells at the migrating front of human glioblastoma

Glioblastoma is the most common primary brain cancer in adults and is notoriously difficult to treat due to its diffuse nature. We performed single-cell RNAseq on 3589 cells in a cohort of four patients. We obtained cells from the tumor core as well as surrounding peripheral tissue. Our analysis revealed cellular variation in the tumors genome and transcriptome, We were able to identify infiltrating neoplastic cells in regions peripheral to the core lesions. Despite the existence of significant heterogeneity among neoplastic cells, we found that infiltrating GBM cells share a consistent gene signature between patients, suggesting a common mechanism of infiltration. Additionally, in investigating the immunological response to the tumors, we found transcriptionally distinct myeloid cell populations residing in the tumor core and the surrounding peritumoral space. Our data provide a detailed dissection of GBM cell types, revealing an abundance of novel information about tumor formation and migration.

cancer biology↗

bioRxiv2017

Single cell transcriptome analysis of human pancreas reveals transcriptional signatures of aging and somatic mutation patterns.

As organisms age, cells accumulate genetic and epigenetic changes that eventually lead to impaired organ function or catastrophic transformation such as cancer. Since aging appears to be a stochastic process of increasing disorder1 cells in an organ will be individually affected in different ways - thus rendering bulk analyses of postmitotic adult tissues difficult to characterize. Here we directly measure the effects of aging in primary human tissue by performing single-cell transcriptome analysis of 2544 human pancreas cells from eight donors spanning six decades of life. We find that islet cells from older donors have increased levels of molecular disorder as measured both by noise in the transcriptome and by the number of cells which display inappropriate hormone expression, revealing a transcriptional instability associated with aging. By further analyzing the spectrum of somatic mutations in single cells, we found a specific age-dependent mutational signature characterized by C to A and C to G transversions. These mutations are indicators of oxidative stress and the signature is absent in single cells from human brain tissue or in a tumor cell line. We have used the single cell measurements of transcriptional noise and mutation level to identify molecular pathways correlated with these changes that could influence human disease. Our results demonstrate the feasibility of using single-cell RNA-seq data from primary cells to derive meaningful insights into the genetic processes that operate on aging human tissue and to determine molecular mechanisms coordinated with these processes.

genomics↗