| 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. |
- 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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| 3. |
- Pirhonen, Juho, et al.
(författare)
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Lipid Metabolic Reprogramming Extends beyond Histologic Tumor Demarcations in Operable Human Pancreatic Cancer
- 2022
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Ingår i: Cancer Research. - 0008-5472. ; 82:21, s. 3932-3949
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Tidskriftsartikel (refereegranskat)abstract
- Pancreatic ductal adenocarcinoma (PDAC) is among the deadliest malignancies and potentially curable only with radical surgical resection at early stages. The tumor microenvironment has been shown to be central to the development and progression of PDAC.A better understanding of how early human PDAC metabolically communicates with its environment and differs from healthy pancreas could help improve PDAC diagnosis and treatment. Here we performed deep proteomic analyses from diagnostic specimens of operable, treatment-naive PDAC patients (n 14), isolating four tissue compartments by laser-capture microdissection: PDAC lesions, tumor-adjacent but morphologically benign exocrine glands, and connective tissues neighboring each of these compartments. Protein and pathway levels were compared between compartments and with control pancreatic proteomes. Selected targets were studied immunohistochemically in the 14 patients and in additional tumor microarrays, and lipid deposition was assessed by nonlinear label-free imaging (n = 16). Widespread downregulation of pancreatic secretory functions was observed, which was paralleled by high cholesterol biosynthetic activity without prominent lipid storage in the neoplastic cells. Stromal compartments harbored ample blood apolipoproteins, indicating abundant microvasculature at the time of tumor removal. The features best differentiating the tumor-adjacent exocrine tissue from healthy control pancreas were defined by upregulation of proteins related to lipid transport. Importantly, histologically benign exocrine regions harbored the most significant prognostic pathways, with proteins involved in lipid transport and metabolism, such as neutral cholesteryl ester hydrolase 1, associating with shorter survival. In conclusion, this study reveals prognostic molecular changes in the exocrine tissue neighboring pancreatic cancer and identifies enhanced lipid transport and metabolism as its defining features.
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