About this Research Topic

Manuscript Submission Deadline 05 October 2022

High-content imaging (HCI) and high-content analysis (HCA) provide flexible scalability to molecular and cellular research. Particularly, HCA is a method that is used in biological research and drug discovery to identify substances such as small molecules, peptides, or RNAi that alter the phenotype of a cell in a desired manner. Both HCI and HCA feature modules to tackle the specific research including objectives, filters, imaging modes, environmental conditions, etc. Due to the revolution limit, more influential tools are desired for cellular and molecular imaging. On the other hand, the imaging systems are to be supported by modern software tools (including the machine learning tools) with modularity to handle HCIs and HCAs that are characterized by tremendous molecular data.

The living cell images along with software modules for HCA provide a quantum of cellular assays. These cellular assays increase the HCI’s throughput and enable medical experts to reach new medical findings and conclusions. The advent of novel machine learning (ML) tools and algorithms enables the user to analyse a large volume of data and perform HCA. Successful applications include analysis of stimulated trans-fluor cells, BPAE cells and mitochondria cells, neurite outgrowth segmentation, spheroid segmentation, granular object detection, nuclear puncta cell mask, heterogeneous cell population, focal-adhesion segmentation, cell cycle measuring, neuron detection, etc. Machine learning (ML) is a multi-disciplinary and interdisciplinary science, which uses computers as tools and is committed to simulating human learning methods in real-time. Recently, ML has achieved great success in cellular/molecular analysis.

This topic aims to report the recent advances in machine learning-guided HCI and HCA for molecular and cellular imaging addressing, but not limited to, the following themes:

• HCI systems

• Machine learning-related techniques in HCA

• Modularity in cellular/molecular imaging modes

• Statistical tools and data analytics in cellular/molecular imaging

• Prediction and classification algorithms for cellular/molecular imaging data

• Clinical diagnosis and treatment

• Neurite outgrowth segmentation

• Cell cycle measuring

• Exploration of cancer immunotherapy targets

• Cell metabolic analysis

Keywords: molecular imaging, automated microscope systems, resolution enhancement system, prediction algorithms, high-content cellular image analysis


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

High-content imaging (HCI) and high-content analysis (HCA) provide flexible scalability to molecular and cellular research. Particularly, HCA is a method that is used in biological research and drug discovery to identify substances such as small molecules, peptides, or RNAi that alter the phenotype of a cell in a desired manner. Both HCI and HCA feature modules to tackle the specific research including objectives, filters, imaging modes, environmental conditions, etc. Due to the revolution limit, more influential tools are desired for cellular and molecular imaging. On the other hand, the imaging systems are to be supported by modern software tools (including the machine learning tools) with modularity to handle HCIs and HCAs that are characterized by tremendous molecular data.

The living cell images along with software modules for HCA provide a quantum of cellular assays. These cellular assays increase the HCI’s throughput and enable medical experts to reach new medical findings and conclusions. The advent of novel machine learning (ML) tools and algorithms enables the user to analyse a large volume of data and perform HCA. Successful applications include analysis of stimulated trans-fluor cells, BPAE cells and mitochondria cells, neurite outgrowth segmentation, spheroid segmentation, granular object detection, nuclear puncta cell mask, heterogeneous cell population, focal-adhesion segmentation, cell cycle measuring, neuron detection, etc. Machine learning (ML) is a multi-disciplinary and interdisciplinary science, which uses computers as tools and is committed to simulating human learning methods in real-time. Recently, ML has achieved great success in cellular/molecular analysis.

This topic aims to report the recent advances in machine learning-guided HCI and HCA for molecular and cellular imaging addressing, but not limited to, the following themes:

• HCI systems

• Machine learning-related techniques in HCA

• Modularity in cellular/molecular imaging modes

• Statistical tools and data analytics in cellular/molecular imaging

• Prediction and classification algorithms for cellular/molecular imaging data

• Clinical diagnosis and treatment

• Neurite outgrowth segmentation

• Cell cycle measuring

• Exploration of cancer immunotherapy targets

• Cell metabolic analysis

Keywords: molecular imaging, automated microscope systems, resolution enhancement system, prediction algorithms, high-content cellular image analysis


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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