Integrating Subclonal Response Heterogeneity to Define Cancer Organoid Therapeutic Sensitivity

Kratz, Jeremy D.; Rehman, Shujah; Johnson, Katherine A.; Gillette, Amani A.; Sunil, Aishwarya; Favreau, Peter F.; Pasch, Cheri A.; Miller, Devon; Zarling, Lucas C.; Yeung, Austin H.; Clipson, Linda; Anderson, Samantha J.; DeZeeuw, Alyssa K.; Sprackling, Carley M.; Lemmon, Kayla K.; Abbott, Daniel E.; Burkard, Mark E.; Bassetti, M.; Eickhoff, Jens; Foley, Eugene F.; Heise, Charles P.; Kimple, Randall J.; Lawson, Elise H.; LoConte, Noelle K.; Lubner, Sam Joseph; Mulkerin, Daniel; Geltzeiler, Cristina B.; Uboha, Nataliya Volodymyrivna; McIlwain, Sean J.; Ong, Irene M.; Carchman, Evie H.; Skala, Melissa C.; Deming, Dustin A.

doi:10.1101/2021.10.15.464556

Cited by 4 publications

(9 citation statements)

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“…6 ). While further studies are warranted, we hypothesize that these conclusions would apply to other epithelial cancer organoid models due to their similarities in culturing 10 , 54 . Further work should determine if these results hold true in other newly established organoid models of other diseases.…”

Section: Discussionmentioning

confidence: 81%

Impact of baseline culture conditions of cancer organoids when determining therapeutic response and tumor heterogeneity

DeStefanis

Kratz

Olson

et al. 2022

Sci Rep

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Representative models are needed to screen new therapies for patients with cancer. Cancer organoids are a leap forward as a culture model that faithfully represents the disease. Mouse-derived cancer organoids (MDCOs) are becoming increasingly popular, however there has yet to be a standardized method to assess therapeutic response and identify subpopulation heterogeneity. There are multiple factors unique to organoid culture that could affect how therapeutic response and MDCO heterogeneity are assessed. Here we describe an analysis of nearly 3500 individual MDCOs where individual organoid morphologic tracking was performed. Change in MDCO diameter was assessed in the presence of control media or targeted therapies. Individual organoid tracking was identified to be more sensitive to treatment response than well-level assessment. The impact of different generations of mice of the same genotype, different regions of the colon, and organoid specific characteristics including baseline size, passage number, plating density, and location within the matrix were examined. Only the starting size of the MDCO altered the subsequent growth. These results were corroborated using ~ 1700 patient-derived cancer organoids (PDCOs) isolated from 19 patients. Here we establish organoid culture parameters for individual organoid morphologic tracking to determine therapeutic response and growth/response heterogeneity for translational studies.

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Section: Discussionmentioning

confidence: 81%

Impact of baseline culture conditions of cancer organoids when determining therapeutic response and tumor heterogeneity

DeStefanis

Kratz

Olson

et al. 2022

Sci Rep

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“…(a) Representative NAD(P)H intensity images of rectal PDCOs (images acquired from Kratz et al 27 ) with morphologies defined as circular or irregular and dense or void. Masks are overlain for manual segmentation (orange) and Cellpose segmentation (blue) where more overlap (green) results in a higher dice score.…”

Section: Resultsmentioning

confidence: 99%

“…Autofluorescence FLIM is also a useful tool for measuring treatment response in 3D PDCO cultures, where cell borders become blurred, and cells become more irregular as they compact into dense PDCO structures 24,27 . Autofluorescence FLIM parameters across multiple treatments remain similar for Cellpose and manual segmentation of PDCOs (Fig.…”

Section: Metabolic Parameters Are Preserved Using Cellpose Segmentationmentioning

confidence: 99%

“…3D PDCOs. All PDCO images were acquired from a previously published dataset in Kratz et al 27 Briefly, imaging protocol was the same as for PANC-1, though differences existed for laser [Mai Tai DeepSee Ti:sapphire laser (Coherent, Inc)] and NAD(P)H bandpass filter (440/80 nm).…”

Section: Two-photon Flimmentioning

confidence: 99%

“…The training encompassed observing a series of annotated NAD(P)H intensity images spanning 2D and 3D cultures and then segmenting those images with guidance given through comparisons of the annotated images with their segmentation. After manual identification of nuclei, the cytoplasm region was isolated using a Python script to propagate out from the masked nuclei by 8 µm (10 pixels) to match the protocol of prior studies where we employed manual segmentation 23,27 . The reported segmentation time was determined using a stopwatch from the opening of an image set to the closing of an image set and dividing by the number of images in the set.…”

Section: Image Segmentationmentioning

confidence: 99%

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Cellpose as a reliable method for single-cell segmentation of autofluorescence microscopy images

Riendeau,

Gillette,

Guzman

et al. 2024

Preprint

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Autofluorescence microscopy uses intrinsic sources of molecular contrast to provide cellular-level information without extrinsic labels. However, traditional cell segmentation tools are often optimized for high signal-to-noise ratio (SNR) images, such as fluorescently labeled cells, and unsurprisingly perform poorly on low SNR autofluorescence images. Therefore, new cell segmentation tools are needed for autofluorescence microscopy. Cellpose is a deep learning network that is generalizable across diverse cell microscopy images and automatically segments single cells to improve throughput and reduce inter-human biases. This study aims to validate Cellpose for autofluorescence imaging, specifically from multiphoton intensity images of NAD(P)H. Manually segmented nuclear masks of NAD(P)H images were used to train new Cellpose models. These models were applied to PANC-1 cells treated with metabolic inhibitors and patient-derived cancer organoids (across 9 patients) treated with chemotherapies. These datasets include co-registered fluorescence lifetime imaging microscopy (FLIM) of NAD(P)H and FAD, so fluorescence decay parameters and the optical redox ratio (ORR) were compared between masks generated by the new Cellpose model and manual segmentation. The Dice score between repeated manually segmented masks was significantly lower than that of repeated Cellpose masks (p<0.0001) indicating greater reproducibility between Cellpose masks. There was also a high correlation (R2>0.9) between Cellpose and manually segmented masks for the ORR, mean NAD(P)H lifetime, and mean FAD lifetime across 2D and 3D cell culture treatment conditions. Masks generated from Cellpose and manual segmentation also maintain similar means, variances, and effect sizes between treatments for the ORR and FLIM parameters. Overall, Cellpose provides a fast, reliable, reproducible, and accurate method to segment single cells in autofluorescence microscopy images such that functional changes in cells are accurately captured in both 2D and 3D culture.

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Technologies to Assess Drug Response and Heterogeneity in Patient-Derived Cancer Organoids

Skala

Deming

Kratz

2022

Annu. Rev. Biomed. Eng.

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Patient-derived cancer organoids (PDCOs) are organotypic 3D cultures grown from patient tumor samples. PDCOs provide an exciting opportunity to study drug response and heterogeneity within and between patients and this research can guide new drug development and inform clinical treatment planning. We review technologies to assess PDCO drug response and heterogeneity, discuss best practices for clinically relevant drug screens, and assert the importance of quantifying single-cell and organoid heterogeneity to characterize response. Autofluorescence imaging of PDCO growth and metabolic activity is highlighted as a compelling method to monitor single-cell and single-organoid response robustly and reproducibly. We also speculate on the future of PDCOs in clinical practice and drug discovery. Future development will require standardization of assessment methods for both morphology and function in PDCOs, increased throughput for new drug development, prospective validation with patient outcomes, and robust classification algorithms. Expected final online publication date for the Annual Review of Biomedical Engineering, Volume 24 is June 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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Integrating Subclonal Response Heterogeneity to Define Cancer Organoid Therapeutic Sensitivity

Cited by 4 publications

References 76 publications

Impact of baseline culture conditions of cancer organoids when determining therapeutic response and tumor heterogeneity

Impact of baseline culture conditions of cancer organoids when determining therapeutic response and tumor heterogeneity

Cellpose as a reliable method for single-cell segmentation of autofluorescence microscopy images

Technologies to Assess Drug Response and Heterogeneity in Patient-Derived Cancer Organoids

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