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Article: Three-dimensional reconstruction of fetal rhesus macaque kidneys at single-cell resolution reveals complex inter-relation of structures.

Dequiedt, Lucie / Forjaz, André / Lo, Jamie O / McCarty, Owen / Wu, Pei-Hsun / Rosenberg, Avi / Wirtz, Denis / Kiemen, Ashley

bioRxiv : the preprint server for biology

2024

Abstract: Kidneys are among the most structurally complex organs in the body. Their architecture is critical to ensure proper function and is often impacted by diseases such as diabetes and hypertension. Understanding the spatial interplay between the different ... ...

Abstract	Kidneys are among the most structurally complex organs in the body. Their architecture is critical to ensure proper function and is often impacted by diseases such as diabetes and hypertension. Understanding the spatial interplay between the different structures of the nephron and renal vasculature is crucial. Recent efforts have demonstrated the value of three-dimensional (3D) imaging in revealing new insights into the various components of the kidney; however, these studies used antibodies or autofluorescence to detect structures and so were limited in their ability to compare the many subtle structures of the kidney at once. Here, through 3D reconstruction of fetal rhesus macaque kidneys at cellular resolution, we demonstrate the power of deep learning in exhaustively labelling seventeen microstructures of the kidney. Using these tissue maps, we interrogate the spatial distribution and spatial correlation of the glomeruli, renal arteries, and the nephron. This work demonstrates the power of deep learning applied to 3D tissue images to improve our ability to compare many microanatomical structures at once, paving the way for further works investigating renal pathologies.
Language	English
Publishing date	2024-04-03
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.12.07.570622
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Article ; Online: Cell Trafficking at the Intersection of the Tumor-Immune Compartments.

Du, Wenxuan / Nair, Praful / Johnston, Adrian / Wu, Pei-Hsun / Wirtz, Denis

Annual review of biomedical engineering

2022 Volume 24, Page(s) 275–305

Abstract: Migration is an essential cellular process that regulates human organ development and homeostasis as well as disease initiation and progression. In cancer, immune and tumor cell migration is strongly associated with immune cell infiltration, immune ... ...

Abstract	Migration is an essential cellular process that regulates human organ development and homeostasis as well as disease initiation and progression. In cancer, immune and tumor cell migration is strongly associated with immune cell infiltration, immune escape, and tumor cell metastasis, which ultimately account for more than 90% of cancer deaths. The biophysics and molecular regulation of the migration of cancer and immune cells have been extensively studied separately. However, accumulating evidence indicates that, in the tumor microenvironment, the motilities of immune and cancer cells are highly interdependent via secreted factors such as cytokines and chemokines. Tumor and immune cells constantly express these soluble factors, which produce a tightly intertwined regulatory network for these cells' respective migration. A mechanistic understanding of the reciprocal regulation of soluble factor-mediated cell migration can provide critical information for the development of new biomarkers of tumor progression and of tumor response to immuno-oncological treatments. We review the biophysical andbiomolecular basis for the migration of immune and tumor cells and their associated reciprocal regulatory network. We also describe ongoing attempts to translate this knowledge into the clinic.
MeSH term(s)	Cell Movement ; Chemokines/metabolism ; Humans ; Immunotherapy ; Neoplasms/therapy ; Tumor Microenvironment
Chemical Substances	Chemokines
Language	English
Publishing date	2022-04-06
Publishing country	United States
Document type	Journal Article ; Review ; Research Support, N.I.H., Extramural
ZDB-ID	1448425-0
ISSN	1545-4274 ; 1523-9829
ISSN (online)	1545-4274
ISSN	1523-9829
DOI	10.1146/annurev-bioeng-110320-110749
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Article: Power-law growth models explain incidences and sizes of pancreatic cancer precursor lesions and confirm spatial genomic findings.

Kiemen, Ashley L / Wu, Pei-Hsun / Braxton, Alicia M / Cornish, Toby C / Hruban, Ralph H / Wood, Laura / Wirtz, Denis / Zwicker, David

bioRxiv : the preprint server for biology

2023

Abstract: Pancreatic ductal adenocarcinoma is a rare but lethal cancer. Recent evidence reveals that pancreatic intraepithelial neoplasms (PanINs), the microscopic precursor lesions in the pancreatic ducts that can give rise to invasive pancreatic cancer, are ... ...

Abstract	Pancreatic ductal adenocarcinoma is a rare but lethal cancer. Recent evidence reveals that pancreatic intraepithelial neoplasms (PanINs), the microscopic precursor lesions in the pancreatic ducts that can give rise to invasive pancreatic cancer, are significantly larger and more prevalent than previously believed. Better understanding of the growth law dynamics of PanINs may improve our ability to understand how a miniscule fraction of these lesions makes the transition to invasive cancer. Here, using artificial intelligence (AI)-based three-dimensional (3D) tissue mapping method, we measured the volumes of >1,000 PanIN and found that lesion size is distributed according to a power law with a fitted exponent of -1.7 over > 3 orders of magnitude. Our data also suggest that PanIN growth is not very sensitive to the pancreatic microenvironment or an individual's age, family history, and lifestyle, and is rather shaped by general growth behavior. We analyze several models of PanIN growth and fit the predicted size distributions to the observed data. The best fitting models suggest that both intraductal spread of PanIN lesions and fusing of multiple lesions into large, highly branched structures drive PanIN growth patterns. This work lays the groundwork for future mathematical modeling efforts integrating PanIN incidence, morphology, genomic, and transcriptomic features to understand pancreas tumorigenesis, and demonstrates the utility of combining experimental measurement of human tissues with dynamic modeling for understanding cancer tumorigenesis.
Language	English
Publishing date	2023-12-04
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.12.01.569633
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Article: Three-dimensional assessments are necessary to determine the true, spatially-resolved composition of tissues.

Forjaz, André / Vaz, Eduarda / Romero, Valentina Matos / Joshi, Saurabh / Braxton, Alicia M / Jiang, Ann C / Fujikura, Kohei / Cornish, Toby / Hong, Seung-Mo / Hruban, Ralph H / Wu, Pei-Hsun / Wood, Laura D / Kiemen, Ashley L / Wirtz, Denis

bioRxiv : the preprint server for biology

2024

Abstract: Methods for spatially resolved cellular profiling using thinly cut sections have enabled in-depth quantitative tissue mapping to study inter-sample and intra-sample differences in normal human anatomy and disease onset and progression. These methods ... ...

Abstract	Methods for spatially resolved cellular profiling using thinly cut sections have enabled in-depth quantitative tissue mapping to study inter-sample and intra-sample differences in normal human anatomy and disease onset and progression. These methods often profile extremely limited regions, which may impact the evaluation of heterogeneity due to tissue sub-sampling. Here, we applied CODA, a deep learning-based tissue mapping platform, to reconstruct the three-dimensional (3D) microanatomy of grossly normal and cancer-containing human pancreas biospecimens obtained from individuals who underwent pancreatic resection. To compare inter- and intra-sample heterogeneity, we assessed bulk and spatially resolved tissue composition in a cohort of two-dimensional (2D) whole slide images (WSIs) and a cohort of thick slabs of pancreas tissue that were digitally reconstructed in 3D from serial sections. To demonstrate the marked under sampling of 2D assessments, we simulated the number of WSIs and tissue microarrays (TMAs) necessary to represent the compositional heterogeneity of 3D data within 10% error to reveal that tens of WSIs and hundreds of TMA cores are sometimes needed. We show that spatial correlation of different pancreatic structures decay significantly within a span of microns, demonstrating that 2D histological sections may not be representative of their neighboring tissues. In sum, we demonstrate that 3D assessments are necessary to accurately assess tissue composition in normal and abnormal specimens and in order to accurately determine neoplastic content. These results emphasize the importance of intra-sample heterogeneity in tissue mapping efforts.
Language	English
Publishing date	2024-03-28
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.12.04.569986
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Article: Generative interpolation and restoration of images using deep learning for improved 3D tissue mapping.

Joshi, Saurabh / Forjaz, André / Han, Kyu Sang / Shen, Yu / Queiroga, Vasco / Xenes, Daniel / Matelsk, Jordan / Wester, Brock / Barrutia, Arrate Munoz / Kiemen, Ashley L / Wu, Pei-Hsun / Wirtz, Denis

bioRxiv : the preprint server for biology

2024

Abstract: The development of novel imaging platforms has improved our ability to collect and analyze large three-dimensional (3D) biological imaging datasets. Advances in computing have led to an ability to extract complex spatial information from these data, such ...

Abstract	The development of novel imaging platforms has improved our ability to collect and analyze large three-dimensional (3D) biological imaging datasets. Advances in computing have led to an ability to extract complex spatial information from these data, such as the composition, morphology, and interactions of multi-cellular structures, rare events, and integration of multi-modal features combining anatomical, molecular, and transcriptomic (among other) information. Yet, the accuracy of these quantitative results is intrinsically limited by the quality of the input images, which can contain missing or damaged regions, or can be of poor resolution due to mechanical, temporal, or financial constraints. In applications ranging from intact imaging (e.g. light-sheet microscopy and magnetic resonance imaging) to sectioning based platforms (e.g. serial histology and serial section transmission electron microscopy), the quality and resolution of imaging data has become paramount. Here, we address these challenges by leveraging frame interpolation for large image motion (FILM), a generative AI model originally developed for temporal interpolation, for spatial interpolation of a range of 3D image types. Comparative analysis demonstrates the superiority of FILM over traditional linear interpolation to produce functional synthetic images, due to its ability to better preserve biological information including microanatomical features and cell counts, as well as image quality, such as contrast, variance, and luminance. FILM repairs tissue damages in images and reduces stitching artifacts. We show that FILM can decrease imaging time by synthesizing skipped images. We demonstrate the versatility of our method with a wide range of imaging modalities (histology, tissue-clearing/light-sheet microscopy, magnetic resonance imaging, serial section transmission electron microscopy), species (human, mouse), healthy and diseased tissues (pancreas, lung, brain), staining techniques (IHC, H&E), and pixel resolutions (8 nm, 2 μm, 1mm). Overall, we demonstrate the potential of generative AI in improving the resolution, throughput, and quality of biological image datasets, enabling improved 3D imaging.
Language	English
Publishing date	2024-03-28
Publishing country	United States
Document type	Preprint
DOI	10.1101/2024.03.07.583909
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Article: Characterization of the immobilized algae-based bioreactor with external ceramic ultrafiltration membrane to remove nutrients from the synthetic secondary wastewater effluent

Wu, Pei-Hsun / Hsieh, Tsung-Min / Wu, Hung-Yu / Yu, Chang-Ping

International biodeterioration & biodegradation. 2021 Oct., v. 164

2021

Abstract: In this study, alginate-immobilization of microalgae combined with continuous/intermittent algae-based membrane bioreactor (A-MBR) and external ceramic membrane was developed to investigate the effect of immobilization on algal growth, nutrient removal, ... ...

Abstract	In this study, alginate-immobilization of microalgae combined with continuous/intermittent algae-based membrane bioreactor (A-MBR) and external ceramic membrane was developed to investigate the effect of immobilization on algal growth, nutrient removal, algal organic matter (AOM) and membrane fouling control in synthetic secondary wastewater effluent treatment. First, Chlorella vulgaris and Scenedesmus quadricauda were selected to investigate the treatment performance, AOM characteristics and membrane fouling control between suspended and immobilized microalgae. The results showed the effect of immobilization on two microalgae growth was not significant and nearly complete NO₃⁻-N and PO₄³⁻-P removals were achieved by both immobilized microalgae in batch experiments, contributing from algal uptake as well as chemical precipitation. In addition, fluorescence excitation-emission matrix spectra indicated specific intensity of protein- and soluble microbial byproduct-like peaks in AOM derived from immobilized S. quadricauda was lower than C. vulgaris and reduced recently produced AOM from two immobilized microalgae was observed, which would improve membrane fouling control. After 35-day intermittent A-MBR operation with immobilized S. quadricauda, higher concentration of microalgal biomass and 63% NO₃⁻-N and 58% PO₄³⁻-P removals were achieved. In addition, alginate-immobilization and intermittent operation for relaxation of membrane showed slower decline of permeate flux under constant pressure mode and therefore, were beneficial for membrane fouling control. Hence, the intermittent A-MBR with immobilized microalgae has potential to cultivate microalgae, provide stable treatment performance to remove nutrients and limit membrane fouling in secondary wastewater effluent treatment.
Keywords	Chlorella vulgaris ; Scenedesmus quadricauda ; biodegradation ; biomass ; ceramics ; chemical precipitation ; fluorescence ; membrane bioreactors ; microalgae ; organic matter ; ultrafiltration ; wastewater
Language	English
Dates of publication	2021-10
Publishing place	Elsevier Ltd
Document type	Article
ISSN	0964-8305
DOI	10.1016/j.ibiod.2021.105309
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei.

Phillip, Jude M / Han, Kyu-Sang / Chen, Wei-Chiang / Wirtz, Denis / Wu, Pei-Hsun

Nature protocols

2021 Volume 16, Issue 2, Page(s) 754–774

Abstract: Cell morphology encodes essential information on many underlying biological processes. It is commonly used by clinicians and researchers in the study, diagnosis, prognosis, and treatment of human diseases. Quantification of cell morphology has seen ... ...

Abstract	Cell morphology encodes essential information on many underlying biological processes. It is commonly used by clinicians and researchers in the study, diagnosis, prognosis, and treatment of human diseases. Quantification of cell morphology has seen tremendous advances in recent years. However, effectively defining morphological shapes and evaluating the extent of morphological heterogeneity within cell populations remain challenging. Here we present a protocol and software for the analysis of cell and nuclear morphology from fluorescence or bright-field images using the VAMPIRE algorithm ( https://github.com/kukionfr/VAMPIRE_open ). This algorithm enables the profiling and classification of cells into shape modes based on equidistant points along cell and nuclear contours. Examining the distributions of cell morphologies across automatically identified shape modes provides an effective visualization scheme that relates cell shapes to cellular subtypes based on endogenous and exogenous cellular conditions. In addition, these shape mode distributions offer a direct and quantitative way to measure the extent of morphological heterogeneity within cell populations. This protocol is highly automated and fast, with the ability to quantify the morphologies from 2D projections of cells seeded both on 2D substrates or embedded within 3D microenvironments, such as hydrogels and tissues. The complete analysis pipeline can be completed within 60 minutes for a dataset of ~20,000 cells/2,400 images.
MeSH term(s)	Algorithms ; Cell Nucleus/physiology ; Cell Shape/physiology ; Humans ; Imaging, Three-Dimensional/methods ; Microscopy, Confocal/methods ; Software ; Unsupervised Machine Learning/statistics & numerical data
Language	English
Publishing date	2021-01-11
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2244966-8
ISSN	1750-2799 ; 1754-2189
ISSN (online)	1750-2799
ISSN	1754-2189
DOI	10.1038/s41596-020-00432-x
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Dissecting cellular mechanics: Implications for aging, cancer, and immunity.

Harris, Michael J / Wirtz, Denis / Wu, Pei-Hsun

Seminars in cell & developmental biology

2018 Volume 93, Page(s) 16–25

Abstract: Cells are dynamic structures that must respond to complex physical and chemical signals from their surrounding environment. The cytoskeleton is a key mediator of a cell's response to the signals of both the extracellular matrix and other cells present in ...

Abstract	Cells are dynamic structures that must respond to complex physical and chemical signals from their surrounding environment. The cytoskeleton is a key mediator of a cell's response to the signals of both the extracellular matrix and other cells present in the local microenvironment and allows it to tune its own mechanical properties in response to these cues. A growing body of evidence suggests that altered cellular viscoelasticity is a strong indicator of disease state; including cancer, laminopathy (genetic disorders of the nuclear lamina), infection, and aging. Here, we review recent work on the characterization of cell mechanics in disease and discuss the implications of altered viscoelasticity in regulation of immune responses. Finally, we provide an overview of techniques for measuring the mechanical properties of cells deeply embedded within tissues.
MeSH term(s)	Aging ; Cells/immunology ; Cells/pathology ; Humans ; Immunity ; Neoplasms/pathology ; Viscosity
Language	English
Publishing date	2018-10-30
Publishing country	England
Document type	Journal Article ; Review
ZDB-ID	1312473-0
ISSN	1096-3634 ; 1084-9521
ISSN (online)	1096-3634
ISSN	1084-9521
DOI	10.1016/j.semcdb.2018.10.008
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Article ; Online: Deep learning identification of stiffness markers in breast cancer.

Sneider, Alexandra / Kiemen, Ashley / Kim, Joo Ho / Wu, Pei-Hsun / Habibi, Mehran / White, Marissa / Phillip, Jude M / Gu, Luo / Wirtz, Denis

Biomaterials

2022 Volume 285, Page(s) 121540

Abstract: While essential to our understanding of solid tumor progression, the study of cell and tissue mechanics has yet to find traction in the clinic. Determining tissue stiffness, a mechanical property known to promote a malignant phenotype in vitro and in ... ...

Abstract	While essential to our understanding of solid tumor progression, the study of cell and tissue mechanics has yet to find traction in the clinic. Determining tissue stiffness, a mechanical property known to promote a malignant phenotype in vitro and in vivo, is not part of the standard algorithm for the diagnosis and treatment of breast cancer. Instead, clinicians routinely use mammograms to identify malignant lesions and radiographically dense breast tissue is associated with an increased risk of developing cancer. Whether breast density is related to tumor tissue stiffness, and what cellular and non-cellular components of the tumor contribute the most to its stiffness are not well understood. Through training of a deep learning network and mechanical measurements of fresh patient tissue, we create a bridge in understanding between clinical and mechanical markers. The automatic identification of cellular and extracellular features from hematoxylin and eosin (H&E)-stained slides reveals that global and local breast tissue stiffness best correlate with the percentage of straight collagen. Importantly, the percentage of dense breast tissue does not directly correlate with tissue stiffness or straight collagen content.
MeSH term(s)	Breast Density ; Breast Neoplasms/pathology ; Collagen ; Deep Learning ; Female ; Humans ; Mammography
Chemical Substances	Collagen (9007-34-5)
Language	English
Publishing date	2022-04-27
Publishing country	Netherlands
Document type	Journal Article ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, N.I.H., Extramural
ZDB-ID	603079-8
ISSN	1878-5905 ; 0142-9612
ISSN (online)	1878-5905
ISSN	0142-9612
DOI	10.1016/j.biomaterials.2022.121540
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Article: Substrate stiffness modulates the emergence and magnitude of senescence phenotypes.

Starich, Bartholomew / Yang, Fan / Tanrioven, Derin / Kung, Heng-Chung / Baek, Joanne / Nair, Praful R / Kamat, Pratik / Macaluso, Nico / Eoh, Joon / Han, Kyu Sang / Gu, Luo / Sun, Sean / Wu, Pei-Hsun / Wirtz, Denis / Phillip, Jude M

bioRxiv : the preprint server for biology

2024

Abstract: Cellular senescence is a major driver of aging and disease. Here we show that substrate stiffness modulates the emergence and magnitude of senescence phenotypes post induction. Using a primary dermal fibroblast model of senescence, we show that decreased ...

Abstract	Cellular senescence is a major driver of aging and disease. Here we show that substrate stiffness modulates the emergence and magnitude of senescence phenotypes post induction. Using a primary dermal fibroblast model of senescence, we show that decreased substrate stiffness accelerates cell-cycle arrest during senescence development and regulate expression of conventional protein-based biomarkers of senescence. We found that the expression of these senescence biomarkers, namely p21
Language	English
Publishing date	2024-02-07
Publishing country	United States
Document type	Preprint
DOI	10.1101/2024.02.06.579151
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