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  1. Article ; Online: MIM-CyCIF: masked imaging modeling for enhancing cyclic immunofluorescence (CyCIF) with panel reduction and imputation.

    Sims, Zachary / Mills, Gordon B / Chang, Young Hwan

    Communications biology

    2024  Volume 7, Issue 1, Page(s) 409

    Abstract: Cyclic Immunofluorescence (CyCIF) can quantify multiple biomarkers, but panel capacity is limited by technical challenges. We propose a computational panel reduction approach that can impute the information content from 25 markers using only 9 markers, ... ...

    Abstract Cyclic Immunofluorescence (CyCIF) can quantify multiple biomarkers, but panel capacity is limited by technical challenges. We propose a computational panel reduction approach that can impute the information content from 25 markers using only 9 markers, learning co-expression and morphological patterns while concurrently increasing speed and panel content and decreasing cost. We demonstrate strong correlations in predictions and generalizability across breast and colorectal cancer, illustrating applicability of our approach to diverse tissue types.
    MeSH term(s) Fluorescent Antibody Technique ; Diagnostic Imaging
    Language English
    Publishing date 2024-04-03
    Publishing country England
    Document type Journal Article
    ISSN 2399-3642
    ISSN (online) 2399-3642
    DOI 10.1038/s42003-024-06110-y
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: MIM-CyCIF: Masked Imaging Modeling for Enhancing Cyclic Immunofluorescence (CyCIF) with Panel Reduction and Imputation.

    Chang, Young Hwan / Sims, Zachary / Mills, Gordon

    Research square

    2023  

    Abstract: CyCIF can quantify multiple biomarkers, but panel capacity is limited by technical challenges. We propose a computational panel reduction approach that can impute the information content from 25 markers using only 9 markers, learning co-expression and ... ...

    Abstract CyCIF can quantify multiple biomarkers, but panel capacity is limited by technical challenges. We propose a computational panel reduction approach that can impute the information content from 25 markers using only 9 markers, learning co-expression and morphological patterns while concurrently increasing speed and panel content and decreasing cost. We demonstrate strong correlations in predictions and generalizability across breast and colorectal cancer, illustrating applicability of our approach to diverse tissue types.
    Language English
    Publishing date 2023-09-21
    Publishing country United States
    Document type Preprint
    DOI 10.21203/rs.3.rs-3270272/v1
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article: MIM-CyCIF: Masked Imaging Modeling for Enhancing Cyclic Immunofluorescence (CyCIF) with Panel Reduction and Imputation.

    Sims, Zachary / Mills, Gordon B / Chang, Young Hwan

    bioRxiv : the preprint server for biology

    2023  

    Abstract: CyCIF quantifies multiple biomarkers, but panel capacity is compromised by technical challenges including tissue loss. We propose a computational panel reduction, inferring surrogate CyCIF data from a subset of biomarkers. Our model reconstructs the ... ...

    Abstract CyCIF quantifies multiple biomarkers, but panel capacity is compromised by technical challenges including tissue loss. We propose a computational panel reduction, inferring surrogate CyCIF data from a subset of biomarkers. Our model reconstructs the information content from 25 markers using only 9 markers, learning co-expression and morphological patterns. We demonstrate strong correlations in predictions and generalizability across breast and colorectal cancer tissue microarrays, illustrating broader applicability to diverse tissue types.
    Language English
    Publishing date 2023-08-16
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2023.05.10.540265
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article: Strategies to Reduce Long-Term Drug Resistance by Considering Effects of Differential Selective Treatments.

    Asnaashari, Tina Ghodsi / Chang, Young Hwan

    Mathematical and computational oncology : third international symposium, ISMCO 2021, virtual event, October 11-13, 2021 : proceedings. ISMCO (Symposium) (3rd : 2021 : Online)

    2021  Volume 13060, Page(s) 49–60

    Abstract: Despite great advances in modeling and cancer therapy using optimal control theory, tumor heterogeneity and drug resistance are major obstacles in cancer treatments. Since recent biological studies demonstrated the evidence of tumor heterogeneity and ... ...

    Abstract Despite great advances in modeling and cancer therapy using optimal control theory, tumor heterogeneity and drug resistance are major obstacles in cancer treatments. Since recent biological studies demonstrated the evidence of tumor heterogeneity and assessed potential biological and clinical implications, tumor heterogeneity should be taken into account in the optimal control problem to improve treatment strategies. Here, first we study the effects of two different treatment strategies (i.e., symmetric and asymmetric) in a minimal two-population model to examine the long-term effects of these treatment methods on the system. Second, by considering tumor adaptation to treatment as a factor of the cost function, the optimal treatment strategy is derived. Numerical examples show that optimal treatment decreases tumor burden for the long-term by decreasing rate of tumor adaptation over time.
    Language English
    Publishing date 2021-12-12
    Publishing country Switzerland
    Document type Journal Article
    DOI 10.1007/978-3-030-91241-3_5
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article: SEG: Segmentation Evaluation in absence of Ground truth labels.

    Sims, Zachary / Strgar, Luke / Thirumalaisamy, Dharani / Heussner, Robert / Thibault, Guillaume / Chang, Young Hwan

    bioRxiv : the preprint server for biology

    2023  

    Abstract: Identifying individual cells or nuclei is often the first step in the analysis of multiplex tissue imaging (MTI) data. Recent efforts to produce plug-and-play, end-to-end MTI analysis tools such as ... ...

    Abstract Identifying individual cells or nuclei is often the first step in the analysis of multiplex tissue imaging (MTI) data. Recent efforts to produce plug-and-play, end-to-end MTI analysis tools such as MCMICRO
    Language English
    Publishing date 2023-02-24
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2023.02.23.529809
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article: Single-cell morphodynamical trajectories enable prediction of gene expression accompanying cell state change.

    Copperman, Jeremy / Mclean, Ian C / Gross, Sean M / Chang, Young Hwan / Zuckerman, Daniel M / Heiser, Laura M

    bioRxiv : the preprint server for biology

    2024  

    Abstract: Extracellular signals induce changes to molecular programs that modulate multiple cellular phenotypes, including proliferation, motility, and differentiation status. The connection between dynamically adapting phenotypic states and the molecular programs ...

    Abstract Extracellular signals induce changes to molecular programs that modulate multiple cellular phenotypes, including proliferation, motility, and differentiation status. The connection between dynamically adapting phenotypic states and the molecular programs that define them is not well understood. Here we develop data-driven models of single-cell phenotypic responses to extracellular stimuli by linking gene transcription levels to "morphodynamics" - changes in cell morphology and motility observable in time-lapse image data. We adopt a dynamics-first view of cell state by grouping single-cell trajectories into states with shared morphodynamic responses. The single-cell trajectories enable development of a first-of-its-kind computational approach to map live-cell dynamics to snapshot gene transcript levels, which we term MMIST, Molecular and Morphodynamics-Integrated Single-cell Trajectories. The key conceptual advance of MMIST is that cell behavior can be quantified based on dynamically defined states and that extracellular signals alter the overall distribution of cell states by altering rates of switching between states. We find a cell state landscape that is bound by epithelial and mesenchymal endpoints, with distinct sequences of epithelial to mesenchymal transition (EMT) and mesenchymal to epithelial transition (MET) intermediates. The analysis yields predictions for gene expression changes consistent with curated EMT gene sets and provides a prediction of thousands of RNA transcripts through extracellular signal-induced EMT and MET with near-continuous time resolution. The MMIST framework leverages true single-cell dynamical behavior to generate molecular-level omics inferences and is broadly applicable to other biological domains, time-lapse imaging approaches and molecular snapshot data.
    Language English
    Publishing date 2024-01-19
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2024.01.18.576248
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Morphodynamical cell state description via live-cell imaging trajectory embedding.

    Copperman, Jeremy / Gross, Sean M / Chang, Young Hwan / Heiser, Laura M / Zuckerman, Daniel M

    Communications biology

    2023  Volume 6, Issue 1, Page(s) 484

    Abstract: Time-lapse imaging is a powerful approach to gain insight into the dynamic responses of cells, but the quantitative analysis of morphological changes over time remains challenging. Here, we exploit the concept of "trajectory embedding" to analyze ... ...

    Abstract Time-lapse imaging is a powerful approach to gain insight into the dynamic responses of cells, but the quantitative analysis of morphological changes over time remains challenging. Here, we exploit the concept of "trajectory embedding" to analyze cellular behavior using morphological feature trajectory histories-that is, multiple time points simultaneously, rather than the more common practice of examining morphological feature time courses in single timepoint (snapshot) morphological features. We apply this approach to analyze live-cell images of MCF10A mammary epithelial cells after treatment with a panel of microenvironmental perturbagens that strongly modulate cell motility, morphology, and cell cycle behavior. Our morphodynamical trajectory embedding analysis constructs a shared cell state landscape revealing ligand-specific regulation of cell state transitions and enables quantitative and descriptive models of single-cell trajectories. Additionally, we show that incorporation of trajectories into single-cell morphological analysis enables (i) systematic characterization of cell state trajectories, (ii) better separation of phenotypes, and (iii) more descriptive models of ligand-induced differences as compared to snapshot-based analysis. This morphodynamical trajectory embedding is broadly applicable to the quantitative analysis of cell responses via live-cell imaging across many biological and biomedical applications.
    MeSH term(s) Ligands ; Diagnostic Imaging ; Single-Cell Analysis ; Cell Movement ; Epithelial Cells
    Chemical Substances Ligands
    Language English
    Publishing date 2023-05-04
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
    ISSN 2399-3642
    ISSN (online) 2399-3642
    DOI 10.1038/s42003-023-04837-8
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: Quantitative image analysis pipeline for detecting circulating hybrid cells in immunofluorescence images with human-level accuracy.

    Heussner, Robert T / Whalen, Riley M / Anderson, Ashley / Theison, Heather / Baik, Joseph / Gibbs, Summer / Wong, Melissa H / Chang, Young Hwan

    Cytometry. Part A : the journal of the International Society for Analytical Cytology

    2024  

    Abstract: Circulating hybrid cells (CHCs) are a newly discovered, tumor-derived cell population found in the peripheral blood of cancer patients and are thought to contribute to tumor metastasis. However, identifying CHCs by immunofluorescence (IF) imaging of ... ...

    Abstract Circulating hybrid cells (CHCs) are a newly discovered, tumor-derived cell population found in the peripheral blood of cancer patients and are thought to contribute to tumor metastasis. However, identifying CHCs by immunofluorescence (IF) imaging of patient peripheral blood mononuclear cells (PBMCs) is a time-consuming and subjective process that currently relies on manual annotation by laboratory technicians. Additionally, while IF is relatively easy to apply to tissue sections, its application to PBMC smears presents challenges due to the presence of biological and technical artifacts. To address these challenges, we present a robust image analysis pipeline to automate the detection and analysis of CHCs in IF images. The pipeline incorporates quality control to optimize specimen preparation protocols and remove unwanted artifacts, leverages a β-variational autoencoder (VAE) to learn meaningful latent representations of single-cell images, and employs a support vector machine (SVM) classifier to achieve human-level CHC detection. We created a rigorously labeled IF CHC data set including nine patients and two disease sites with the assistance of 10 annotators to evaluate the pipeline. We examined annotator variation and bias in CHC detection and provided guidelines to optimize the accuracy of CHC annotation. We found that all annotators agreed on CHC identification for only 65% of the cells in the data set and had a tendency to underestimate CHC counts for regions of interest (ROIs) containing relatively large amounts of cells (>50,000) when using the conventional enumeration method. On the other hand, our proposed approach is unbiased to ROI size. The SVM classifier trained on the β-VAE embeddings achieved an F1 score of 0.80, matching the average performance of human annotators. Our pipeline enables researchers to explore the role of CHCs in cancer progression and assess their potential as a clinical biomarker for metastasis. Further, we demonstrate that the pipeline can identify discrete cellular phenotypes among PBMCs, highlighting its utility beyond CHCs.
    Language English
    Publishing date 2024-02-22
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2099868-5
    ISSN 1552-4930 ; 0196-4763 ; 1552-4922
    ISSN (online) 1552-4930
    ISSN 0196-4763 ; 1552-4922
    DOI 10.1002/cyto.a.24826
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: Ultra high content analyses of circulating and tumor associated hybrid cells reveal phenotypic heterogeneity.

    Whalen, Riley M / Anderson, Ashley N / Jones, Jocelyn A / Sims, Zachary / Chang, Young Hwan / Nederlof, Michel A / Wong, Melissa H / Gibbs, Summer L

    Scientific reports

    2024  Volume 14, Issue 1, Page(s) 7350

    Abstract: Persistently high, worldwide mortality from cancer highlights the unresolved challenges of disease surveillance and detection that impact survival. Development of a non-invasive, blood-based biomarker would transform survival from cancer. We demonstrate ... ...

    Abstract Persistently high, worldwide mortality from cancer highlights the unresolved challenges of disease surveillance and detection that impact survival. Development of a non-invasive, blood-based biomarker would transform survival from cancer. We demonstrate the functionality of ultra-high content analyses of a newly identified population of tumor cells that are hybrids between neoplastic and immune cells in patient matched tumor and peripheral blood specimens. Using oligonucleotide conjugated antibodies (Ab-oligo) permitting cyclic immunofluorescence (cyCIF), we present analyses of phenotypes among tumor and peripheral blood hybrid cells. Interestingly, the majority of circulating hybrid cell (CHC) subpopulations were not identified in tumor-associated hybrids. These results highlight the efficacy of ultra-high content phenotypic analyses using Ab-oligo based cyCIF applied to both tumor and peripheral blood specimens. The combination of a multiplex phenotypic profiling platform that is gentle enough to analyze blood to detect and evaluate disseminated tumor cells represents a novel approach to exploring novel tumor biology and potential utility for developing the population as a blood-based biomarker in cancer.
    MeSH term(s) Humans ; Neoplastic Cells, Circulating/pathology ; Biomarkers, Tumor ; Hybrid Cells/pathology ; Antibodies ; Phenotype
    Chemical Substances Biomarkers, Tumor ; Antibodies
    Language English
    Publishing date 2024-03-28
    Publishing country England
    Document type Journal Article
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-024-57381-8
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: Computational multiplex panel reduction to maximize information retention in breast cancer tissue microarrays.

    Ternes, Luke / Lin, Jia-Ren / Chen, Yu-An / Gray, Joe W / Chang, Young Hwan

    PLoS computational biology

    2022  Volume 18, Issue 9, Page(s) e1010505

    Abstract: Recent state-of-the-art multiplex imaging techniques have expanded the depth of information that can be captured within a single tissue sample by allowing for panels with dozens of markers. Despite this increase in capacity, space on the panel is still ... ...

    Abstract Recent state-of-the-art multiplex imaging techniques have expanded the depth of information that can be captured within a single tissue sample by allowing for panels with dozens of markers. Despite this increase in capacity, space on the panel is still limited due to technical artifacts, tissue loss, and long imaging acquisition time. As such, selecting which markers to include on a panel is important, since removing important markers will result in a loss of biologically relevant information, but identifying redundant markers will provide a room for other markers. To address this, we propose computational approaches to determine the amount of shared information between markers and select an optimally reduced panel that captures maximum amount of information with the fewest markers. Here we examine several panel selection approaches and evaluate them based on their ability to reconstruct the full panel images and information within breast cancer tissue microarray datasets using cyclic immunofluorescence as a proof of concept. We show that all methods perform adequately and can re-capture cell types using only 18 of 25 markers (72% of the original panel size). The correlation-based selection methods achieved the best single-cell marker mean intensity predictions with a Spearman correlation of 0.90 with the reduced panel. Using the proposed methods shown here, it is possible for researchers to design more efficient multiplex imaging panels that maximize the amount of information retained with the limited number of markers with respect to certain evaluation metrics and architecture biases.
    MeSH term(s) Artifacts ; Biomarkers ; Breast Neoplasms ; Female ; Humans
    Chemical Substances Biomarkers
    Language English
    Publishing date 2022-09-30
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 2193340-6
    ISSN 1553-7358 ; 1553-734X
    ISSN (online) 1553-7358
    ISSN 1553-734X
    DOI 10.1371/journal.pcbi.1010505
    Database MEDical Literature Analysis and Retrieval System OnLINE

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