| 1. |
- Brasko, Csilla, et al.
(författare)
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Intelligent image-based in situ single-cell isolation
- 2018
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Ingår i: Nature Communications. - : NATURE PUBLISHING GROUP. - 2041-1723. ; 9
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Tidskriftsartikel (refereegranskat)abstract
- Quantifying heterogeneities within cell populations is important for many fields including cancer research and neurobiology; however, techniques to isolate individual cells are limited. Here, we describe a high-throughput, non-disruptive, and cost-effective isolation method that is capable of capturing individually targeted cells using widely available techniques. Using high-resolution microscopy, laser microcapture microscopy, image analysis, and machine learning, our technology enables scalable molecular genetic analysis of single cells, targetable by morphology or location within the sample.
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| 2. |
- Firsova, Alexandra, et al.
(författare)
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Topographic atlas of cell states identifies regional gene expression in the adult human lung
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Single cell mRNA sequencing of the whole organ has become a popular technique to reveal rare types and subtypes of previously characterized cells as well as to distinguish and characterize gene expression of previously unknown cell types. Unsupervised clustering can reveal tens or even hundreds of variable genes that characterize cell types. Variation in gene expression is often observed within one cell type, and sometimes cannot be biologically explained without mapping of mRNA on tissue. In this study we aim to (i) map the majority of cell types of human lung, (ii) describe variability in their gene expression and (iii) relate this gene expression to cellular location and neighborhoods. Using three different spatial transcriptomics approaches, we mapped epithelial cell states of airways and submucosal gland, and defined cell type-unrelated gene expression variability along proximo-distal axis, including potential regulators and co-regulators of such cell states in the mesenchymal and immune cell niches. In addition, we mapped rare cell types, such as subtypes of neuroendocrine cells, ionocytes and tuft (brush) cells, revealing tracheal preference for ionocytes, and distal airways for GHRL-positive neuroendocrine cells. Finally, we used the created map as a reference for the diseased tissue from patients with stage II COPD and revealed perturbed cell states and COPD-specific imbalance of cell types, affecting immune and AT0 clusters.
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| 3. |
- Piccinini, Filippo, et al.
(författare)
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Advanced Cell Classifier : User-Friendly Machine-Learning-Based Software for Discovering Phenotypes in High-Content Imaging Data
- 2017
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Ingår i: CELL SYSTEMS. - : CELL PRESS. - 2405-4712. ; 4:6, s. 651-
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Tidskriftsartikel (refereegranskat)abstract
- High-content, imaging-based screens now routinely generate data on a scale that precludes manual verification and interrogation. Software applying machine learning has become an essential tool to automate analysis, but these methods require annotated examples to learn from. Efficiently exploring large datasets to find relevant examples remains a challenging bottleneck. Here, we present Advanced Cell Classifier (ACC), a graphical software package for phenotypic analysis that addresses these difficulties. ACC applies machine-learning and image-analysis methods to high-content data generated by large-scale, cell-based experiments. It features methods to mine microscopic image data, discover new phenotypes, and improve recognition performance. We demonstrate that these features substantially expedite the training process, successfully uncover rare phenotypes, and improve the accuracy of the analysis. ACC is extensively documented, designed to be user-friendly for researchers without machine-learning expertise, and distributed as a free open-source tool at www.cellclassifier.org.
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| 4. |
- Smith, Kevin, 1975-, et al.
(författare)
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Phenotypic Image Analysis Software Tools for Exploring and Understanding Big Image Data from Cell-Based Assays
- 2018
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Ingår i: CELL SYSTEMS. - : Elsevier. - 2405-4712. ; 6:6, s. 636-653
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Forskningsöversikt (refereegranskat)abstract
- Phenotypic image analysis is the task of recognizing variations in cell properties using microscopic image data. These variations, produced through a complex web of interactions between genes and the environment, may hold the key to uncover important biological phenomena or to understand the response to a drug candidate. Today, phenotypic analysis is rarely performed completely by hand. The abundance of high-dimensional image data produced by modern high-throughput microscopes necessitates computational solutions. Over the past decade, a number of software tools have been developed to address this need. They use statistical learning methods to infer relationships between a cell's phenotype and data from the image. In this review, we examine the strengths and weaknesses of non-commercial phenotypic image analysis software, cover recent developments in the field, identify challenges, and give a perspective on future possibilities.
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