Analysis of Gene Expression Profiles of Microdissected Cell Populations Indicates that Testicular Carcinoma In situ Is an Arrested Gonocyte

ABSTRACTIn multicellular organisms, individual cells are characterized by their gene expression profiles and the spatial interactions among cells enable the elaboration of complex functions. Expression profiling in spatially defined regions is crucial to elucidate cell interactions and functions. Here, we established a transcriptome profiling method coupled with photo-isolation chemistry (PIC) that allows the determination of expression profiles specifically from photo-irradiated regions of whole tissues. PIC uses photo-caged oligodeoxynucleotides for in situ reverse transcription. After photo-irradiation of limited areas, gene expression was detected from at least 10 cells in the tissue sections. PIC transcriptome analysis detected genes specifically expressed in small distinct areas of the mouse embryo. Thus, PIC enables transcriptome profiles to be determined from limited regions at a spatial resolution up to the diffraction limit.

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PDGFRα and αSMA mark two distinct mesenchymal cell populations involved in parenchymal and vascular remodeling in pulmonary fibrosis

AJP Lung Cellular and Molecular Physiology ◽

10.1152/ajplung.00128.2019 ◽

2020 ◽

Vol 318 (4) ◽

pp. L684-L697 ◽

Cited By ~ 7

Author(s):

Valentina Biasin ◽

Slaven Crnkovic ◽

Anita Sahu-Osen ◽

Anna Birnhuber ◽

Elie El Agha ◽

...

Keyword(s):

Gene Expression ◽

Pulmonary Fibrosis ◽

Expression Profiles ◽

Smooth Muscle Actin ◽

Gene Expression Profiles ◽

Mesenchymal Cell ◽

Cellular Heterogeneity ◽

Cell Populations ◽

Variable Expression ◽

Distinct Gene

Pulmonary fibrosis is characterized by pronounced collagen deposition and myofibroblast expansion, whose origin and plasticity remain elusive. We utilized a fate-mapping approach to investigate α-smooth muscle actin (αSMA)+ and platelet-derived growth factor receptor α (PDGFRα)+ cells in two lung fibrosis models, complemented by cell type-specific next-generation sequencing and investigations on human lungs. Our data revealed that αSMA+ and PDGFRα+ cells mark two distinct mesenchymal lineages with minimal transdifferentiation potential during lung fibrotic remodeling. Parenchymal and perivascular fibrotic regions were populated predominantly with PDGFRα+ cells expressing collagen, while αSMA+ cells in the parenchyma and vessel wall showed variable expression of collagen and the contractile protein desmin. The distinct gene expression profile found in normal conditions was retained during pathologic remodeling. Cumulatively, our findings identify αSMA+ and PDGFRα+ cells as two separate lineages with distinct gene expression profiles in adult lungs. This cellular heterogeneity suggests that anti-fibrotic therapy should target diverse cell populations.

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Gene Expression Profiling Reveals Fundamental Differences between Leukemic Stem Cells (Lin-CD34+CD38−) and Mature Blasts (Lin−CD34+CD38+) of Acute Myeloid Leukaemia (AML) Patients.

Blood ◽

10.1182/blood.v106.11.1377.1377 ◽

2005 ◽

Vol 106 (11) ◽

pp. 1377-1377

Author(s):

Kazem Zibara ◽

Daniel Pearce ◽

David Taussig ◽

Spyros Skoulakis ◽

Simon Tomlinson ◽

...

Keyword(s):

Gene Expression ◽

Stem Cells ◽

Gene Expression Profiling ◽

Expression Profiling ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Cell Populations ◽

Leukemic Stem Cells ◽

Significant Differential Expression ◽

New Genes

Abstract The identification of LSC has important implications for future research as well as for the development of novel therapies. The phenotypic description of LSC now enables their purification and should facilitate the identification of genes that are preferentially expressed in these cells compared to normal HSC. However, gene-expression profiling is usually conducted on mononuclear cells of AML patients from either peripheral blood and/or bone marrow. These samples contain a mixture of blasts cells, normal hematopoietic cells and limited number of leukemic stem cells. Thus, this results in a composite profile that obscure differences between LSC and blasts cells with low proliferative potential. The aim of this study was to compare the gene expression profile of highly purified LSC versus leukemic blasts in order to identify genes that might have important roles in driving the leukemia. For this purpose, we analyzed the gene expression profiles of highly purified LSCs (Lin−CD34+CD38−) and more mature blast cells (Lin−CD34+CD38+) isolated from 7 adult AML patients. All samples were previously tested for the ability of the Lin−CD34+CD38− cells but not the Lin−CD34+CD38+ fraction to engraft using the non-obese diabetic/severe combined immuno-deficiency (NOD-SCID) repopulation assay. Affymetrix microarrays (U133A chip), containing 22,283 genes, were used for the analysis. Comparison of Lin-CD34+CD38- cell population to the Lin−CD34+CD38+ cell fraction showed 5421 genes to be expressed in both fractions. Comparative analysis of gene-expression profiles showed statistically significant differential expression of 133 genes between the 2 cell populations. Most of the genes were downregulated in the LSC-enriched fraction, compared to the more differentiated fraction. Gene ontology was used to determine the categories of the up-regulated transcripts. These transcripts, which are selectively expressed, include a number of known genes (e.g., receptors, signalling genes, proliferation and cell cycle genes and transcription factors). These genes play important roles in differentiation, self-renewal, migration and adhesion of HSCs. Among the genes showing the highest differences in expression levels were the following: ribonucleotide reductase M2 polypeptide, thymidylate synthetase, ZW10 interactor, cathepsin G, azurocidin 1, topoisomerase II, CDC20, nucleolar and spindle associated protein 1, Rac GTPase activating protein 1, leukocyte immunoglobulin-like receptor, proliferating cell nuclear antigen, myeloperoxidase, cyclin A1 (RRM2, TYMS, ZWINT, CTSG, AZU1, TOP2A, CDC20, NUSAP1, RACGAP1, LILRB2, PCNA, MPO, CCNA1). Some transcripts detected have not been implicated in HSC functions, and others have unknown function so far. This work identifies new genes that might play a role in leukemogenesis and cancer stem cells. It also leads to a better description and understanding of the molecular phenotypes of these 2 cell populations. Hence, in addition to being a more efficient way to further understand the biology of LSC, this should also provide a more efficient way of identifying new therapeutics and diagnostic targets.

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Self-reporting transposons enable simultaneous readout of gene expression and transcription factor binding in single cells

10.1101/538553 ◽

2019 ◽

Cited By ~ 3

Author(s):

Arnav Moudgil ◽

Michael N. Wilkinson ◽

Xuhua Chen ◽

June He ◽

Alex J. Cammack ◽

...

Keyword(s):

Gene Expression ◽

Transcription Factor ◽

Single Cell ◽

Binding Sites ◽

Expression Profiles ◽

Single Cells ◽

Gene Expression Profiles ◽

Cell Types ◽

Specific Cell

AbstractIn situ measurements of transcription factor (TF) binding are confounded by cellular heterogeneity and represent averaged profiles in complex tissues. Single cell RNA-seq (scRNA-seq) is capable of resolving different cell types based on gene expression profiles, but no technology exists to directly link specific cell types to the binding pattern of TFs in those cell types. Here, we present self-reporting transposons (SRTs) and their use in single cell calling cards (scCC), a novel assay for simultaneously capturing gene expression profiles and mapping TF binding sites in single cells. First, we show how the genomic locations of SRTs can be recovered from mRNA. Next, we demonstrate that SRTs deposited by the piggyBac transposase can be used to map the genome-wide localization of the TFs SP1, through a direct fusion of the two proteins, and BRD4, through its native affinity for piggyBac. We then present the scCC method, which maps SRTs from scRNA-seq libraries, thus enabling concomitant identification of cell types and TF binding sites in those same cells. As a proof-of-concept, we show recovery of cell type-specific BRD4 and SP1 binding sites from cultured cells. Finally, we map Brd4 binding sites in the mouse cortex at single cell resolution, thus establishing a new technique for studying TF biology in situ.

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GENE-47. A 3D ATLAS TO EVALUATE THE SPATIAL PATTERNING OF GENETIC ALTERATIONS AND TUMOR CELL STATES IN GLIOMA

Neuro-Oncology ◽

10.1093/neuonc/noz175.449 ◽

2019 ◽

Vol 21 (Supplement_6) ◽

pp. vi107-vi108

Author(s):

Stephanie Hilz ◽

Chibo Hong ◽

Llewellyn Jalbert ◽

Tali Mazor ◽

Michael Martin ◽

...

Keyword(s):

Gene Expression ◽

Expression Profiles ◽

Expression Patterns ◽

Gene Expression Profiles ◽

Genetic Alterations ◽

Cell Populations ◽

Low Grade ◽

Lower Grade ◽

Diffuse Glioma ◽

Spatial Patterning

Abstract BACKGROUND Previous studies of solid tumors have been restricted in their ability to map how heterogeneous cell populations evolved within the tumor in three-dimensional (3D) space due to insufficient sampling, typically one sample per tumor, and a lack of knowledge of where within the tumor the sample was obtained. Knowledge of the extensivity of heterogeneity and how it is spatially distributed is crucial for assessing whether a therapeutic target is truly tumor-wide, and for exploring how mutations relate to heterogeneity in the local microenvironment. METHODS We developed a novel platform to integrate and visualize in 3D multi-omics data generated from each of 8–10 spatially mapped samples per tumor. Together, the 171 samples collected using this approach from 16 adult diffuse glioma at diagnosis and recurrence form a novel resource – the 3D Glioma Atlas. RESULTS By maximally sampling the tumor geography without excluding samples based on low cancer cell fraction (CCF), we identify a subpopulation of glioblastoma with pervasively lower CCF likely excluded by other atlases, such as the TCGA, that used stringent CCF cutoffs. Exome sequencing of 3D-mapped samples from lower-grade tumors revealed that clonal expansions are typically spatially segregated, implying minimal tumor-wide intermixing of genetically heterogenous cells. Heterogeneity is less spatially segregated for faster-growing high-grade tumors, suggesting that cell populations expand in these tumors differently. Recurrent low-grade tumors have greater intratumoral mutational heterogeneity than newly diagnosed tumors, though this did not translate into greater dissimilarity in gene expression profiles for recurrent tumors, suggesting minimal functional impact of this additional mutational diversity on gene expression. CONCLUSIONS The delineation of spatial patterns of heterogeneity that our work provides enables more informed interpretation of biopsies and greater insight into the factors shaping intratumoral variation of gene expression patterns. Ongoing work is exploring the spatial patterning of amplification events and the tumor microenvironment.

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Improved gene expression signature of testicular carcinoma in situ

International Journal of Andrology ◽

10.1111/j.1365-2605.2007.00758.x ◽

2007 ◽

Vol 30 (4) ◽

pp. 292-303 ◽

Cited By ~ 36

Author(s):

Kristian Almstrup ◽

Henrik Leffers ◽

Ragnhild A. Lothe ◽

Niels E. Skakkebæk ◽

Si B. Sonne ◽

...

Keyword(s):

Gene Expression ◽

Carcinoma In Situ ◽

Gene Expression Signature ◽

Expression Signature ◽

Testicular Carcinoma

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Analysis and simulation of gene expression profiles in pure and mixed cell populations

Physical Biology ◽

10.1088/1478-3975/8/3/035013 ◽

2011 ◽

Vol 8 (3) ◽

pp. 035013 ◽

Cited By ~ 14

Author(s):

Daniel Hebenstreit ◽

Sarah A Teichmann

Keyword(s):

Gene Expression ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Cell Populations ◽

Mixed Cell

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Genome-wide gene expression profiling of testicular carcinoma in situ progression into overt tumours

British Journal of Cancer ◽

10.1038/sj.bjc.6602560 ◽

2005 ◽

Vol 92 (10) ◽

pp. 1934-1941 ◽

Cited By ~ 66

Author(s):

K Almstrup ◽

C E Hoei-Hansen ◽

J E Nielsen ◽

U Wirkner ◽

W Ansorge ◽

...

Keyword(s):

Gene Expression ◽

Gene Expression Profiling ◽

Expression Profiling ◽

Carcinoma In Situ ◽

Genome Wide ◽

Testicular Carcinoma ◽

Genome Wide Gene Expression

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Leukemic and Normal Stem Cell Transcriptional Signatures Determined by Functional Assays Are Predictive of the Overall Survival of AML Patients.

Blood ◽

10.1182/blood.v114.22.389.389 ◽

2009 ◽

Vol 114 (22) ◽

pp. 389-389

Author(s):

Kolja Eppert ◽

Katsuto Takenaka ◽

Björn Nilsson ◽

Eric R Lechman ◽

Vicki Ling ◽

...

Keyword(s):

Gene Expression ◽

Stem Cells ◽

Overall Survival ◽

Stem Cell ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Cell Populations ◽

Hematopoietic Stem ◽

Functional Assays ◽

Stem Cell Populations

Abstract Abstract 389 Normal hematopoiesis and acute myeloid leukemia (AML) are organized as hierarchies with stem cells, which possess extensive self-renewal and proliferative capacity, at the apex. Although there is definitive evidence from experimental models for the existence of leukemic stem cells (LSC) in some human leukemias, the relevance of LSC to human disease progression is still lacking. While chemotherapeutic treatment of AML patients typically results in disease remission, the majority of patients will eventually relapse and succumb to the disease, indicating that residual LSC are not eliminated by current treatment. We hypothesize that stem cell derived gene expression profiles may be more clinically relevant than those derived from examination of bulk leukemia samples. Here we show the clinical significance of novel stem cell related expression profiles derived from 25 functionally validated human leukemia stem cell populations and 6 normal hematopoietic stem cell populations. Little is currently known about the molecular regulatory networks that govern human LSC or hematopoietic stem cells (HSC). Therefore, we have carried out global mRNA gene expression profiling of FACS sorted subpopulations of cells enriched for human stem cells, progenitor cells and mature cells from 16 AML primary patient samples and 3 cord blood samples to investigate these pathways. Similar to normal hematopoietic stem cells, leukemia stem, progenitor and mature cells can be sorted using CD34 and CD38 markers. Due to the heterogeneous nature of AML, it is vital that quantitative functional assays are used to characterize the LSC and progenitor activity in each sorted fraction. In vitro cell suspension cultures and methylcellulose colony formation assays were performed to characterize progenitor and blast populations. Importantly, we applied a novel and improved in vivo SCID leukemia initiating cell assay to substantiate the presence of LSC activity in each sorted fraction of 16 AML patient samples. With this enhanced assay, LSC were detected in the expected CD34+/CD38- population. However, in the majority of AML samples, LSC were detected in at least one additional fraction, demonstrating the importance of functional validation when interpreting global gene expression profiles of sorted stem cell populations. LSC and HSC specific signatures were identified following a statistical analysis that compared fractions with stem cell activity against those without (25 LSC vs 29 non-LSC; 6 HSC vs 6 non-HSC). When applied to an independent gene expression data set from 160 cytogenetically normal AML samples, a 25 probe LSC signature was the strongest predictor of overall survival (p<0.0001, HR=2.6, 95%CI 1.8-4.0, median survival 236 vs 999 days; Figure 1a). Furthermore, the 225 probe HSC specific signature derived from normal cells also provided a strong predictor of survival (p<0.0001, HR=2.3, 95%CI 1.5-3.4, median survival 238 vs 741 days; Figure 1b). We queried the gene expression-based chemical genomic database Connectivity Map with the LSC-related gene list and found a negative correlation between the genes in the LSC profile and the expression of genes that are transcriptionally induced following treatment with common chemotherapeutic compounds such as doxorubicin, suggesting resistance to chemotherapy as one possible mechanism for the correlation of the stem cell signatures with survival. Together these data support the hypothesis that the biological determinants that underlie stemness in both normal and leukemic cells are predictors of poor outcome, and are potential targets for novel therapy. Disclosures: No relevant conflicts of interest to declare.

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Automated identification of the mouse brain’s spatial compartments from in situ sequencing data

BMC Biology ◽

10.1186/s12915-020-00874-5 ◽

2020 ◽

Vol 18 (1) ◽

Author(s):

Gabriele Partel ◽

Markus M. Hilscher ◽

Giorgia Milli ◽

Leslie Solorzano ◽

Anna H. Klemm ◽

...

Keyword(s):

Gene Expression ◽

Mouse Brain ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Cell Types ◽

Sequencing Data ◽

Tissue Samples ◽

Gene Markers ◽

Smooth Transitions

Abstract Background Neuroanatomical compartments of the mouse brain are identified and outlined mainly based on manual annotations of samples using features related to tissue and cellular morphology, taking advantage of publicly available reference atlases. However, this task is challenging since sliced tissue sections are rarely perfectly parallel or angled with respect to sections in the reference atlas and organs from different individuals may vary in size and shape and requires manual annotation. With the advent of in situ sequencing technologies and automated approaches, it is now possible to profile the gene expression of targeted genes inside preserved tissue samples and thus spatially map biological processes across anatomical compartments. Results Here, we show how in situ sequencing data combined with dimensionality reduction and clustering can be used to identify spatial compartments that correspond to known anatomical compartments of the brain. We also visualize gradients in gene expression and sharp as well as smooth transitions between different compartments. We apply our method on mouse brain sections and show that a fully unsupervised approach can computationally define anatomical compartments, which are highly reproducible across individuals, using as few as 18 gene markers. We also show that morphological variation does not always follow gene expression, and different spatial compartments can be defined by various cell types with common morphological features but distinct gene expression profiles. Conclusion We show that spatial gene expression data can be used for unsupervised and unbiased annotations of mouse brain spatial compartments based only on molecular markers, without the need of subjective manual annotations based on tissue and cell morphology or matching reference atlases.

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