Project description:Clear cell renal cell carcinoma (ccRCC) initiated from the renal epithelium is the most prevalent histological type of adult kidney cancers. Dissecting intratumoral heterogeneity (ITH) of ccRCC has leveraged to extend our knowledge on how primary tumors harboring driver mutations evolve and spread to other sites. The cellular fractions within and across the primary (pRCC) and metastatic RCC (mRCC) are heterogeneous in both their genetic and biological features determining the variability in clinical aggressiveness and sensitivity to the therapy. To achieve sustainable therapeutic benefit with targeted agents in mRCC, the effective target should focus on signaling pathways that are related to driver mutations occurred early in the clonal evolution of the disease and thus should be common to primary tumor and metastatic sites. Considering that extensive genetic heterogeneity may result in drug response variability among patients and treatment resistance, the tailored strategies for metastatic RCC is urgently needed. Here, we analyze single-cell RNA-seq (scRNA-seq) data from a matched primary RCC (pRCC) and lung metastasis (mRCC) to dissect ITH at the highest resolution to date with the objective of discovering the better therapeutic regimen. In order to identify successful clonal propagation from patient to PDX samples and understand pathogenesis from primary to metastatic RCC, we performed whole-exome sequencing (WES, n=4) and matched aCGH (n=4) on bulk tumor samples. And we utilized single-cell RNA sequencing (scRNA-seq) to model and dissect functional heterogeneity acroass primary and metastatic RCC tumors. We checked whether of capturing live one cell, not more cells, in microfluidics by fluorescent microscopic observation. To construct RNA sequencing libraries, we performed further quality controls including adequate quantities and qualities of amplified transcriptomes respectively from single cells. Tumor cells from the parental mRCC (n=34), PDX-mRCC (n=36) and PDX-pRCC (n=46) were finally analyzed in this study after filtering out poor quality cells.

Project description:The experimental evolution of laboratory populations of microbes provides an opportunity to observe the evolutionary dynamics of adaptation in real time.Until very recently, however, such studies have been limited by our inability to systematically find mutations in evolved organisms.We overcome this limitation by using a variety of DNA microarray-based techniques to characterize genetic changes, including point mutations, structural changes, and insertion variation, that resulted from the experimental adaptation of 24 haploid and diploid cultures of Saccharomyces cerevisiae to growth in glucose-, sulfate, or phosphate-limited chemostats for ~ 200 generations.We identified frequent genomic amplifications and rearrangements as well as novel retrotransposition events associated with adaptation.Global mutation detection in 10 clonal isolates identified 32 point mutations. On the basis of mutation frequencies, we infer that these mutations and the subsequent dynamics of adaptation are determined by the batch phase of growth prior to initiation of continuous phase in the chemostat.We relate these genotypic changes to phenotypic outcomes, namely global patterns of gene expression, and to increases in fitness by 5-50%. We found that the spectrum of available mutations in glucose or phosphate-limited environments combined with the batch phase population dynamics early in our experiments to allow several distinct genotypic and phenotypic evolutionary pathways in response to these nutrient limitations. By contrast, sulfate-limited populations were much more constrained in both genotypic and phenotypic outcomes.Thus, the reproducibility of evolution varies with specific selective pressures reflecting the constraints inherent in the system-level organization of metabolic processes in the cell.We were able to relate some of the observed adaptive mutations (e.g. transporter gene amplifications) to known features of the relevant metabolic pathways, but many of the mutations pointed to genes not previously associated with the relevant physiology. Thus, in addition to answering basic mechanistic questions about evolutionary mechanisms our work suggests that experimental evolution can also shed light on the function and regulation of individual metabolic pathways. Keywords: gene expression, CGH, and TSE analysis

Dataset Information

Amplicon of Single-Cell Genotyping of Transcriptomes

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets