Biomedical subjects

Irina Khrebtukova

Publications and source records attributed to Irina Khrebtukova.

7 recordsLinked to original sources

PubMed2006

Massively parallel signature sequencing profiling of fetal human neural precursor cells.

We have examined gene expression in multipotent neural precursor cells (NPCs) derived from human fetal (f) brain tissue and compared its expression profiles with embryonic stem (ESC) cells, embryoid body cell (EBC), and astrocyte precursors using the technique of massively parallel signature sequencing (MPSS). Gene expression profiles show that fNPCs express core neural stem cells markers and share expression profiles with astrocyte precursor cells (APCs) rather than ESC or EBC. Gene expression analysis shows that fNPCs differ from other adult stem and progenitor cells in their marker expression and activation of specific functional networks such as the transforming growth factorbeta (TGFbeta) and Notch signaling pathways. In addition, our results allow us to identify novel genes expressed in fNPCs and provide a detailed profile of fNPCs.

Adult↗

PubMed2006

Massively parallel signature sequencing.

Massively parallel signature sequencing is an ultra-high throughput sequencing technology. It can simultaneously sequence millions of sequence tags, and, therefore, is ideal for whole genome analysis. When applied to expression profiling, it reveals almost every transcript in the sample and provides its accurate expression level. This chapter describes the technology and its application in establishing stem cell transcriptome databases.

Cell Culture Techniques↗

PubMed2005

Evidence for the presence of disease-perturbed networks in prostate cancer cells by genomic and proteomic analyses: a systems approach to disease.

Prostate cancer is initially responsive to androgen ablation therapy and progresses to androgen-unresponsive states that are refractory to treatment. The mechanism of this transition is unknown. A systems approach to disease begins with the quantitative delineation of the informational elements (mRNAs and proteins) in various disease states. We employed two recently developed high-throughput technologies, massively parallel signature sequencing (MPSS) and isotope-coded affinity tag, to gain a comprehensive picture of the changes in mRNA levels and more restricted analysis of protein levels, respectively, during the transition from androgen-dependent LNCaP (model for early-stage prostate cancer) to androgen-independent CL1 cells (model for late-stage prostate cancer). We sequenced >5 million MPSS signatures, obtained >142,000 tandem mass spectra, and built comprehensive MPSS and proteomic databases. The integrated mRNA and protein expression data revealed underlying functional differences between androgen-dependent and androgen-independent prostate cancer cells. The high sensitivity of MPSS enabled us to identify virtually all of the expressed transcripts and to quantify the changes in gene expression between these two cell states, including functionally important low-abundance mRNAs, such as those encoding transcription factors and signal transduction molecules. These data enable us to map the differences onto extant physiologic networks, creating perturbation networks that reflect prostate cancer progression. We found 37 BioCarta and 14 Kyoto Encyclopedia of Genes and Genomes pathways that are up-regulated and 23 BioCarta and 22 Kyoto Encyclopedia of Genes and Genomes pathways that are down-regulated in LNCaP cells versus CL1 cells. Our efforts represent a significant step toward a systems approach to understanding prostate cancer progression.

Cell Line, Tumor↗

PubMed2005

An atlas of human gene expression from massively parallel signature sequencing (MPSS).

We have used massively parallel signature sequencing (MPSS) to sample the transcriptomes of 32 normal human tissues to an unprecedented depth, thus documenting the patterns of expression of almost 20,000 genes with high sensitivity and specificity. The data confirm the widely held belief that differences in gene expression between cell and tissue types are largely determined by transcripts derived from a limited number of tissue-specific genes, rather than by combinations of more promiscuously expressed genes. Expression of a little more than half of all known human genes seems to account for both the common requirements and the specific functions of the tissues sampled. A classification of tissues based on patterns of gene expression largely reproduces classifications based on anatomical and biochemical properties. The unbiased sampling of the human transcriptome achieved by MPSS supports the idea that most human genes have been mapped, if not functionally characterized. This data set should prove useful for the identification of tissue-specific genes, for the study of global changes induced by pathological conditions, and for the definition of a minimal set of genes necessary for basic cell maintenance. The data are available on the Web at http://mpss.licr.org and http://sgb.lynxgen.com.

Algorithms↗

PubMed2005

Transcriptome profiling of human and murine ESCs identifies divergent paths required to maintain the stem cell state.

Human embryonic stem cells (hESCs) are an important source of stem cells in regenerative medicine, and much remains unknown about their molecular characteristics. To develop a detailed genomic profile of ESC lines in two different species, we compared transcriptomes of one murine and two different hESC lines by massively parallel signature sequencing (MPSS). Over 2 million signature tags from each line and their differentiating embryoid bodies were sequenced. Major differences and conserved similarities between species identified by MPSS were validated by reverse transcription polymerase chain reaction (RT-PCR) and microarray. The two hESC lines were similar overall, with differences that are attributable to alleles and propagation. Human-mouse comparisons, however, identified only a small (core) set of conserved genes that included genes known to be important in ESC biology, as well as additional novel genes. Identified were major differences in leukemia inhibitory factor, transforming growth factor-beta, and Wnt and fibroblast growth factor signaling pathways, as well as the expression of genes encoding metabolic, cytoskeletal, and matrix proteins, many of which were verified by RT-PCR or by comparing them with published databases. The study reported here underscores the importance of cross-species comparisons and the versatility and sensitivity of MPSS as a powerful complement to current array technology.

Animals↗

PubMed2004

MPSS profiling of human embryonic stem cells.

BACKGROUND: Pooled human embryonic stem cells (hESC) cell lines were profiled to obtain a comprehensive list of genes common to undifferentiated human embryonic stem cells. RESULTS: Pooled hESC lines were profiled to obtain a comprehensive list of genes common to human ES cells. Massively parallel signature sequencing (MPSS) of approximately three million signature tags (signatures) identified close to eleven thousand unique transcripts, of which approximately 25% were uncharacterised or novel genes. Expression of previously identified ES cell markers was confirmed and multiple genes not known to be expressed by ES cells were identified by comparing with public SAGE databases, EST libraries and parallel analysis by microarray and RT-PCR. Chromosomal mapping of expressed genes failed to identify major hotspots and confirmed expression of genes that map to the X and Y chromosome. Comparison with published data sets confirmed the validity of the analysis and the depth and power of MPSS. CONCLUSIONS: Overall, our analysis provides a molecular signature of genes expressed by undifferentiated ES cells that can be used to monitor the state of ES cells isolated by different laboratories using independent methods and maintained under differing culture conditions

Cell Differentiation↗

PubMed2004

Monitoring early differentiation events in human embryonic stem cells by massively parallel signature sequencing and expressed sequence tag scan.

To identify genes that may be involved in the process of human embryonic stem cell (hESC) differentiation, we profiled gene expression by expressed sequenced tag (EST) enumeration and massively parallel signature sequencing (MPSS) using RNA samples from feeder-free cultures of undifferentiated (passages 40-50) and differentiated (day 14) H1, H7, and H9 lines. MPSS and EST scan analysis showed good concordance and identified a large number of genes that changed rapidly as cultures transition from a pluripotent to a differentiated state. These included known and unknown ES cell-specific genes as well as a large number of known genes that were altered as cells differentiate. A subset of genes that were either up- or down-regulated were selected and their differential expression confirmed by a variety of independent methods, including comparison of expression after further differentiation, publicly available databases, and direct assessments by reverse transcriptase (RT)-PCR and immunocytochemistry. The analysis identified markers unique to the hESC and embryoid bodies (hEBs) stage as well as signaling pathways that likely regulate differentiation. The data generated can be used to monitor the state of hESC isolated by different laboratories using independent methods and maintained under differing culture conditions.

Cell Culture Techniques↗