Quantitative non-invasive cell characterisation and discrimination based on multispectral autofluorescence features

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Abstract

Automated and unbiased methods of non-invasive cell monitoring able to deal with complex biological heterogeneity are fundamentally important for biology and medicine. Label-free cell imaging provides information about endogenous autofluorescent metabolites, enzymes and cofactors in cells. However extracting high content information from autofluorescence imaging has been hitherto impossible. Here, we quantitatively characterise cell populations in different tissue types, live or fixed, by using novel image processing and a simple multispectral upgrade of a wide-field fluorescence microscope. Our optimal discrimination approach enables statistical hypothesis testing and intuitive visualisations where previously undetectable differences become clearly apparent. Label-free classifications are validated by the analysis of Classification Determinant (CD) antigen expression. The versatility of our method is illustrated by detecting genetic mutations in cancer, non-invasive monitoring of CD90 expression, label-free tracking of stem cell differentiation, identifying stem cell subpopulations with varying functional characteristics, tissue diagnostics in diabetes, and assessing the condition of preimplantation embryos.

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Multipotent mesenchymal stromal cells obtained from diverse human tissues share functional properties and gene-expression profile with CD146+ perivascular cells and fibroblasts.

Michelle E. Fontes, Cláudia Zago, Judson Silva … (2008)

The relationship of multipotent mesenchymal stromal cells (MSC) with pericytes and fibroblasts has not been established thus far, although they share many markers of primitive marrow stromal cells and the osteogenic, adipogenic, and chondrogenic differentiation potentials. We compared MSCs from adult or fetal tissues, MSC differentiated in vitro, fibroblasts and cultures of retinal pericytes obtained either by separation with anti-CD146 or adhesion. The characterizations included morphological, immunophenotypic, gene-expression profile, and differentiation potential. Osteogenic, adipocytic, and chondrocytic differentiation was demonstrated for MSC, retinal perivascular cells, and fibroblasts. Cell morphology and the phenotypes defined by 22 markers were very similar. Analysis of the global gene expression obtained by serial analysis of gene expression for 17 libraries and by reverse transcription polymerase chain reaction of 39 selected genes from 31 different cell cultures, revealed similarities among MSC, retinal perivascular cells, and hepatic stellate cells. Despite this overall similarity, there was a heterogeneous expression of genes related to angiogenesis, in MSC derived from veins, artery, perivascular cells, and fibroblasts. Evaluation of typical pericyte and MSC transcripts, such as NG2, CD146, CD271, and CD140B on CD146 selected perivascular cells and MSC by real-time polymerase chain reaction confirm the relationship between these two cell types. Furthermore, the inverse correlation between fibroblast-specific protein-1 and CD146 transcripts observed on pericytes, MSC, and fibroblasts highlight their potential use as markers of this differentiation pathway. Our results indicate that human MSC and pericytes are similar cells located in the wall of the vasculature, where they function as cell sources for repair and tissue maintenance, whereas fibroblasts are more differentiated cells with more restricted differentiation potential.

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Scoring diverse cellular morphologies in image-based screens with iterative feedback and machine learning.

Thouis Jones, Anne Carpenter, Michael Lamprecht … (2009)

Many biological pathways were first uncovered by identifying mutants with visible phenotypes and by scoring every sample in a screen via tedious and subjective visual inspection. Now, automated image analysis can effectively score many phenotypes. In practical application, customizing an image-analysis algorithm or finding a sufficient number of example cells to train a machine learning algorithm can be infeasible, particularly when positive control samples are not available and the phenotype of interest is rare. Here we present a supervised machine learning approach that uses iterative feedback to readily score multiple subtle and complex morphological phenotypes in high-throughput, image-based screens. First, automated cytological profiling extracts hundreds of numerical descriptors for every cell in every image. Next, the researcher generates a rule (i.e., classifier) to recognize cells with a phenotype of interest during a short, interactive training session using iterative feedback. Finally, all of the cells in the experiment are automatically classified and each sample is scored based on the presence of cells displaying the phenotype. By using this approach, we successfully scored images in RNA interference screens in 2 organisms for the prevalence of 15 diverse cellular morphologies, some of which were previously intractable.

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Image-based multivariate profiling of drug responses from single cells.

Lit-Hsin Loo, Lani Wu, Steven J. Altschuler (2007)

Quantitative analytical approaches for discovering new compound mechanisms are required for summarizing high-throughput, image-based drug screening data. Here we present a multivariate method for classifying untreated and treated human cancer cells based on approximately 300 single-cell phenotypic measurements. This classification provides a score, measuring the magnitude of the drug effect, and a vector, indicating the simultaneous phenotypic changes induced by the drug. These two quantities were used to characterize compound activities and identify dose-dependent multiphasic responses. A systematic survey of profiles extracted from a 100-compound compendium of image data revealed that only 10-15% of the original features were required to detect a compound effect. We report the most informative image features for each compound and fluorescence marker set using a method that will be useful for determining minimal collections of readouts for drug screens. Our approach provides human-interpretable profiles and automatic determination of on- and off-target effects.

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Author and article information

Journal

Journal ID (nlm-ta): Sci Rep

Journal ID (iso-abbrev): Sci Rep

Title: Scientific Reports

Publisher: Nature Publishing Group

ISSN (Electronic): 2045-2322

Publication date (Electronic): 31 March 2016

Publication date Collection: 2016

Volume: 6

Electronic Location Identifier: 23453

Affiliations

[1 ]Quantitative Pty Ltd ABN 17165684186, Beaumont Hills NSW 2155, Australia.

[2 ]ARC Centre of Excellence for Nanoscale Biophotonics, Macquarie University , North Ryde 2109, NSW Australia

[3 ]School of Biomedical Sciences, Charles Sturt University, Wagga Wagga, NSW, 2678, Australia

[4 ]Kolling Institute of Medical Research, Royal North Shore Hospital/Northern Clinical School, University of Sydney , Pacific Hwy, St Leonards NSW 2065, Australia

[5 ]Robinson Research Institute, School of Paediatrics and Reproductive Health, The University of Adelaide, Medical School , Frome Road, Adelaide, South Australia, 5005, Australia

[6 ]Australian Research Council Centre of Excellence for Nanoscale Biophotonics and Institute for Photonics and Advanced Sensing, The University of Adelaide, North Terrace , Adelaide, South Australia, 5005, Australia

Author notes

[a ] mg@ 123456quantitative.net.au

[b ] ewa.goldys@ 123456mq.edu.au

Article

Publisher Item ID: srep23453

DOI: 10.1038/srep23453

PMC ID: 4814840

PubMed ID: 27029742

SO-VID: cff62a00-8782-4825-97ac-5613ec55280a

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This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Quantitative non-invasive cell characterisation and discrimination based on multispectral autofluorescence features

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Abstract

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Most cited references 26

Multipotent mesenchymal stromal cells obtained from diverse human tissues share functional properties and gene-expression profile with CD146+ perivascular cells and fibroblasts.

Scoring diverse cellular morphologies in image-based screens with iterative feedback and machine learning.

Image-based multivariate profiling of drug responses from single cells.

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