Microarray

Summary

A microarray is a multiplex lab-on-a-chip. Its purpose is to simultaneously detect the expression of thousands of biological interactions. It is a two-dimensional array on a solid substrate—usually a glass slide or silicon thin-film cell—that assays (tests) large amounts of biological material using high-throughput screening miniaturized, multiplexed and parallel processing and detection methods. The concept and methodology of microarrays was first introduced and illustrated in antibody microarrays (also referred to as antibody matrix) by Tse Wen Chang in 1983 in a scientific publication and a series of patents. The "gene chip" industry started to grow significantly after the 1995 Science Magazine article by the Ron Davis and Pat Brown labs at Stanford University. With the establishment of companies, such as Affymetrix, Agilent, Applied Microarrays, Arrayjet, Illumina, and others, the technology of DNA microarrays has become the most sophisticated and the most widely used, while the

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https://en.wikipedia.org/wiki/Microarray

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An extracellular matrix protein microarray to study endothelial to mesenchymal transformation in mitral valve endothelial cells

Blaise Calpe

In the developing heart, a subset of endothelial cells (ECs) undergo a process of endothelial to mesenchymal transformation (EMT) that leads to the remodeling of the cardiac cushions and the formation of the cardiac valves. Understanding how microenvironmental cues regulate EMT has important implication for the treatment of congenital heart valve diseases and for heart valve tissue engineering. Here a set of extracellular matrix (ECM) proteins and growth factors (GF) were tested to determine the extent of regulation of EMT in adult ovine mitral valve endothelial cells (MVECs). Traditional cell culture dishes present limitations on large scale screening of cells-microenvironments interactions. To address this problem, a high throughput cellular microarray platform was developed. Using a contact printer, 84 different combinations of ECM proteins and GF were microarrayed on a modified glass slide. The slides were seeded with MVECs and immunostained against EMT maker alpha smooth muscle actin ([alpha]-SMA). Fluorescence images of each spot of the microarray were taken with a microscope equipped with an automated stage. These images were analyzed with a custom image processing routine developed with CellProfiler to assess the percentage of cells expressing [alpha]-SMA on each ECM combination.

2011

Spot Identification and Quality Control in Cell-Based Microarrays

Michael Bauer, Blaise Calpe

Cell-based microarrays are being increasingly used as a tool for combinatorial and high throughput screening of cellular microenvironments. Analysis of microarrays requires several steps, including microarray imaging, identification of cell spots, quality control, and data exploration. While high content image analysis, cell counting, and cell pattern recognition methods are established, there is a need for new postprocessing and quality control methods for cell based microarrays used to investigate combinatorial microenvironments. Previously, microarrayed cell spot identification and quality control were performed manually, leading to excessive processing time and potentially resulting in human bias. This work introduces an automated approach to identify cell based microarray spots and spot quality control. The approach was used to analyze the adhesion of murine cardiac side population cells on combinatorial arrays of extracellular matrix proteins. Microarrays were imaged by automated fluorescence microscopy and cells were identified using open source image analysis software (CellProfiler). From these images, clusters of cells making up single cell spots were reliably identified by analyzing the distances between cells using a density-based clustering algorithm (OPTICS). Naive Bayesian classifiers trained on manually scored training sets identified good and poor quality spots using spot size, number of Cells per spot, and cell location as quality control criteria. Combined, the approach identified 78% of high quality spots and 87% of poor quality spots. Full factorial analysis of the, resulting microarray data revealed that collagen IV exhibited the highest positive effect on cell attachment This data processing approach allows for fast and unbiased analysis of cell-based microarray data.

Amer Chemical Soc2012

Androgen-deprivation therapy is the standard treatment for prostate cancer. Despite the initial response, a fraction of cases manifest progression to castration-resistant prostate cancer. PCa recurrence is possibly due to androgen-independent cancer stem cells that reinitiate tumor growth. The properties of cancer stem cells versus highly proliferative progenitor cells remain to be further characterized at the cellular and gene expression level. To address the molecular basis of CSC switch from androgen dependency to independence, we have employed patient-derived xenograft models (LAPC-9 and BM-18) that have different androgen sensitivity properties. We have performed microarray and proteomic analysis of BM-18 tumor tissues prior to and following castration as well as androgen replacement. To characterize the cancer stem cells in these models we have isolated different subpopulations by FACS cytometry, based on combination of selected markers (CD44 and ALDH activity measured by ALDELFUOR assay) aiming to assess the transcriptomic profile of these different populations. To assess response of these CD44+/-/ALDH+/- cells to androgen targeting compounds we have established an androgen-sensitive xenograft model (PNPCa) and tested it by ex vivo tissue culture and organoid viability assays. Proteomic and microarray analysis of bulk BM-18 tumors indicates enrichment of stem cell markers upon castration: CD44, NKX3.1 and ALDH1 isoforms. Readministration of testosterone induces decrease of these markers. Castration induces a rapid tumor volume decrease in the BM-18 as opposed to LAPC-9, reflecting different androgen-dependent cell states. CD44+/-/ALDHhigh/low subpopulations were isolated by flow cytometry and for transcriptomic analysis. Castration in the BM18 model induces an increase in the subpopulations of CD44+/ALDHlow and CD44+/ALDHhigh, while ALDHsingle and ALDHhigh total populations are decreased. Both CD44 expression and ALDH activity is increased in the LAPC-9 model upon castration. Treatment of PNPCa tumor parts ex vivo with enzalutamide inhibited expression of androgen receptor and keratin 8 expression. CD44+ organoids derived from the same tissue did not show altered viability after enzalutamide treatment. Different androgen-independent cancer stem cell populations may be distinguished by ALDH activity status and CD44. Androgen-independent cells in the BM-18 androgen-dependent model are reflected by enrichment of CD44 expression. The least abundant subpopulation CD44+/ALDHhigh is present in the LAPC-9 and expanded upon castration, while in the BM-18 model it is exclusively present after castration, reflecting different androgen sensitivity. Ongoing analysis may elucidate the molecular mechanisms controlling cancer stem cell fates, androgen sensitivity and drug resistance.

AMER ASSOC CANCER RESEARCH2018