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Article ; Online: Weakly Supervised AI for Efficient Analysis of 3D Pathology Samples.

Song, Andrew H / Williams, Mane / Williamson, Drew F K / Jaume, Guillaume / Zhang, Andrew / Chen, Bowen / Serafin, Robert / Liu, Jonathan T C / Baras, Alex / Parwani, Anil V / Mahmood, Faisal

ArXiv

2023

Abstract: Human tissue consists of complex structures that display a diversity of morphologies, forming a tissue microenvironment that is, by nature, three-dimensional (3D). However, the current standard-of-care involves slicing 3D tissue specimens into two- ... ...

Abstract	Human tissue consists of complex structures that display a diversity of morphologies, forming a tissue microenvironment that is, by nature, three-dimensional (3D). However, the current standard-of-care involves slicing 3D tissue specimens into two-dimensional (2D) sections and selecting a few for microscopic evaluation
Language	English
Publishing date	2023-07-27
Publishing country	United States
Document type	Preprint
ISSN	2331-8422
ISSN (online)	2331-8422
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Towards a general-purpose foundation model for computational pathology.

Nature medicine

2024 Volume 30, Issue 3, Page(s) 850–862

Abstract: Quantitative evaluation of tissue images is crucial for computational pathology (CPath) tasks, requiring the objective characterization of histopathological entities from whole-slide images (WSIs). The high resolution of WSIs and the variability of ... ...

Abstract	Quantitative evaluation of tissue images is crucial for computational pathology (CPath) tasks, requiring the objective characterization of histopathological entities from whole-slide images (WSIs). The high resolution of WSIs and the variability of morphological features present significant challenges, complicating the large-scale annotation of data for high-performance applications. To address this challenge, current efforts have proposed the use of pretrained image encoders through transfer learning from natural image datasets or self-supervised learning on publicly available histopathology datasets, but have not been extensively developed and evaluated across diverse tissue types at scale. We introduce UNI, a general-purpose self-supervised model for pathology, pretrained using more than 100 million images from over 100,000 diagnostic H&E-stained WSIs (>77 TB of data) across 20 major tissue types. The model was evaluated on 34 representative CPath tasks of varying diagnostic difficulty. In addition to outperforming previous state-of-the-art models, we demonstrate new modeling capabilities in CPath such as resolution-agnostic tissue classification, slide classification using few-shot class prototypes, and disease subtyping generalization in classifying up to 108 cancer types in the OncoTree classification system. UNI advances unsupervised representation learning at scale in CPath in terms of both pretraining data and downstream evaluation, enabling data-efficient artificial intelligence models that can generalize and transfer to a wide range of diagnostically challenging tasks and clinical workflows in anatomic pathology.
MeSH term(s)	Artificial Intelligence ; Workflow
Language	English
Publishing date	2024-03-19
Publishing country	United States
Document type	Journal Article
ZDB-ID	1220066-9
ISSN	1546-170X ; 1078-8956
ISSN (online)	1546-170X
ISSN	1078-8956
DOI	10.1038/s41591-024-02857-3
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Zs.A 4212: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (2.OG) ab Jg. 2022: Lesesaal (EG)
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Article ; Online: Analysis of 3D pathology samples using weakly supervised AI.

Song, Andrew H / Williams, Mane / Williamson, Drew F K / Chow, Sarah S L / Jaume, Guillaume / Gao, Gan / Zhang, Andrew / Chen, Bowen / Baras, Alexander S / Serafin, Robert / Colling, Richard / Downes, Michelle R / Farré, Xavier / Humphrey, Peter / Verrill, Clare / True, Lawrence D / Parwani, Anil V / Liu, Jonathan T C / Mahmood, Faisal

Cell

2024 Volume 187, Issue 10, Page(s) 2502–2520.e17

Abstract: Human tissue, which is inherently three-dimensional (3D), is traditionally examined through standard-of-care histopathology as limited two-dimensional (2D) cross-sections that can insufficiently represent the tissue due to sampling bias. To holistically ... ...

Abstract	Human tissue, which is inherently three-dimensional (3D), is traditionally examined through standard-of-care histopathology as limited two-dimensional (2D) cross-sections that can insufficiently represent the tissue due to sampling bias. To holistically characterize histomorphology, 3D imaging modalities have been developed, but clinical translation is hampered by complex manual evaluation and lack of computational platforms to distill clinical insights from large, high-resolution datasets. We present TriPath, a deep-learning platform for processing tissue volumes and efficiently predicting clinical outcomes based on 3D morphological features. Recurrence risk-stratification models were trained on prostate cancer specimens imaged with open-top light-sheet microscopy or microcomputed tomography. By comprehensively capturing 3D morphologies, 3D volume-based prognostication achieves superior performance to traditional 2D slice-based approaches, including clinical/histopathological baselines from six certified genitourinary pathologists. Incorporating greater tissue volume improves prognostic performance and mitigates risk prediction variability from sampling bias, further emphasizing the value of capturing larger extents of heterogeneous morphology.
MeSH term(s)	Humans ; Imaging, Three-Dimensional/methods ; Prostatic Neoplasms/pathology ; Prostatic Neoplasms/diagnostic imaging ; Male ; Prognosis ; Deep Learning ; X-Ray Microtomography/methods ; Supervised Machine Learning
Language	English
Publishing date	2024-05-09
Publishing country	United States
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.
ZDB-ID	187009-9
ISSN	1097-4172 ; 0092-8674
ISSN (online)	1097-4172
ISSN	0092-8674
DOI	10.1016/j.cell.2024.03.035
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Zs.A 1167: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (1.OG) ab Jg. 2022: Lesesaal (EG)
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Article ; Online: Pan-cancer integrative histology-genomic analysis via multimodal deep learning.

Chen, Richard J / Lu, Ming Y / Williamson, Drew F K / Chen, Tiffany Y / Lipkova, Jana / Noor, Zahra / Shaban, Muhammad / Shady, Maha / Williams, Mane / Joo, Bumjin / Mahmood, Faisal

Cancer cell

2022 Volume 40, Issue 8, Page(s) 865–878.e6

Abstract: The rapidly emerging field of computational pathology has demonstrated promise in developing objective prognostic models from histology images. However, most prognostic models are either based on histology or genomics alone and do not address how these ... ...

Abstract	The rapidly emerging field of computational pathology has demonstrated promise in developing objective prognostic models from histology images. However, most prognostic models are either based on histology or genomics alone and do not address how these data sources can be integrated to develop joint image-omic prognostic models. Additionally, identifying explainable morphological and molecular descriptors from these models that govern such prognosis is of interest. We use multimodal deep learning to jointly examine pathology whole-slide images and molecular profile data from 14 cancer types. Our weakly supervised, multimodal deep-learning algorithm is able to fuse these heterogeneous modalities to predict outcomes and discover prognostic features that correlate with poor and favorable outcomes. We present all analyses for morphological and molecular correlates of patient prognosis across the 14 cancer types at both a disease and a patient level in an interactive open-access database to allow for further exploration, biomarker discovery, and feature assessment.
MeSH term(s)	Algorithms ; Deep Learning ; Genomics/methods ; Humans ; Neoplasms/genetics ; Neoplasms/pathology ; Prognosis
Language	English
Publishing date	2022-08-08
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
ZDB-ID	2078448-X
ISSN	1878-3686 ; 1535-6108
ISSN (online)	1878-3686
ISSN	1535-6108
DOI	10.1016/j.ccell.2022.07.004
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Zs.A 5650: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (2.OG) ab Jg. 2022: Lesesaal (EG)
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Article ; Online: A General-Purpose Self-Supervised Model for Computational Pathology.

ArXiv

2023

Abstract: Tissue phenotyping is a fundamental computational pathology (CPath) task in learning objective characterizations of histopathologic biomarkers in anatomic pathology. However, whole-slide imaging (WSI) poses a complex computer vision problem in which the ... ...

Abstract	Tissue phenotyping is a fundamental computational pathology (CPath) task in learning objective characterizations of histopathologic biomarkers in anatomic pathology. However, whole-slide imaging (WSI) poses a complex computer vision problem in which the large-scale image resolutions of WSIs and the enormous diversity of morphological phenotypes preclude large-scale data annotation. Current efforts have proposed using pretrained image encoders with either transfer learning from natural image datasets or self-supervised pretraining on publicly-available histopathology datasets, but have not been extensively developed and evaluated across diverse tissue types at scale. We introduce UNI, a general-purpose self-supervised model for pathology, pretrained using over 100 million tissue patches from over 100,000 diagnostic haematoxylin and eosin-stained WSIs across 20 major tissue types, and evaluated on 33 representative CPath clinical tasks in CPath of varying diagnostic difficulties. In addition to outperforming previous state-of-the-art models, we demonstrate new modeling capabilities in CPath such as resolution-agnostic tissue classification, slide classification using few-shot class prototypes, and disease subtyping generalization in classifying up to 108 cancer types in the OncoTree code classification system. UNI advances unsupervised representation learning at scale in CPath in terms of both pretraining data and downstream evaluation, enabling data-efficient AI models that can generalize and transfer to a gamut of diagnostically-challenging tasks and clinical workflows in anatomic pathology.
Language	English
Publishing date	2023-08-29
Publishing country	United States
Document type	Preprint
ISSN	2331-8422
ISSN (online)	2331-8422
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Deep learning-enabled assessment of cardiac allograft rejection from endomyocardial biopsies.

Nature medicine

2022 Volume 28, Issue 3, Page(s) 575–582

Abstract: Endomyocardial biopsy (EMB) screening represents the standard of care for detecting allograft rejections after heart transplant. Manual interpretation of EMBs is affected by substantial interobserver and intraobserver variability, which often leads to ... ...

Abstract	Endomyocardial biopsy (EMB) screening represents the standard of care for detecting allograft rejections after heart transplant. Manual interpretation of EMBs is affected by substantial interobserver and intraobserver variability, which often leads to inappropriate treatment with immunosuppressive drugs, unnecessary follow-up biopsies and poor transplant outcomes. Here we present a deep learning-based artificial intelligence (AI) system for automated assessment of gigapixel whole-slide images obtained from EMBs, which simultaneously addresses detection, subtyping and grading of allograft rejection. To assess model performance, we curated a large dataset from the United States, as well as independent test cohorts from Turkey and Switzerland, which includes large-scale variability across populations, sample preparations and slide scanning instrumentation. The model detects allograft rejection with an area under the receiver operating characteristic curve (AUC) of 0.962; assesses the cellular and antibody-mediated rejection type with AUCs of 0.958 and 0.874, respectively; detects Quilty B lesions, benign mimics of rejection, with an AUC of 0.939; and differentiates between low-grade and high-grade rejections with an AUC of 0.833. In a human reader study, the AI system showed non-inferior performance to conventional assessment and reduced interobserver variability and assessment time. This robust evaluation of cardiac allograft rejection paves the way for clinical trials to establish the efficacy of AI-assisted EMB assessment and its potential for improving heart transplant outcomes.
MeSH term(s)	Allografts ; Artificial Intelligence ; Biopsy ; Deep Learning ; Graft Rejection/diagnosis ; Humans ; Myocardium/pathology
Language	English
Publishing date	2022-03-21
Publishing country	United States
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.
ZDB-ID	1220066-9
ISSN	1546-170X ; 1078-8956
ISSN (online)	1546-170X
ISSN	1078-8956
DOI	10.1038/s41591-022-01709-2
Database	MEDical Literature Analysis and Retrieval System OnLINE

In stock of ZB MED Cologne/Königswinter

Zs.A 4212: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (2.OG) ab Jg. 2022: Lesesaal (EG)
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Book ; Online: Pan-Cancer Integrative Histology-Genomic Analysis via Interpretable Multimodal Deep Learning

Chen, Richard J. / Lu, Ming Y. / Williamson, Drew F. K. / Chen, Tiffany Y. / Lipkova, Jana / Shaban, Muhammad / Shady, Maha / Williams, Mane / Joo, Bumjin / Noor, Zahra / Mahmood, Faisal

2021

Abstract: The rapidly emerging field of deep learning-based computational pathology has demonstrated promise in developing objective prognostic models from histology whole slide images. However, most prognostic models are either based on histology or genomics ... ...

Abstract	The rapidly emerging field of deep learning-based computational pathology has demonstrated promise in developing objective prognostic models from histology whole slide images. However, most prognostic models are either based on histology or genomics alone and do not address how histology and genomics can be integrated to develop joint image-omic prognostic models. Additionally identifying explainable morphological and molecular descriptors from these models that govern such prognosis is of interest. We used multimodal deep learning to integrate gigapixel whole slide pathology images, RNA-seq abundance, copy number variation, and mutation data from 5,720 patients across 14 major cancer types. Our interpretable, weakly-supervised, multimodal deep learning algorithm is able to fuse these heterogeneous modalities for predicting outcomes and discover prognostic features from these modalities that corroborate with poor and favorable outcomes via multimodal interpretability. We compared our model with unimodal deep learning models trained on histology slides and molecular profiles alone, and demonstrate performance increase in risk stratification on 9 out of 14 cancers. In addition, we analyze morphologic and molecular markers responsible for prognostic predictions across all cancer types. All analyzed data, including morphological and molecular correlates of patient prognosis across the 14 cancer types at a disease and patient level are presented in an interactive open-access database (http://pancancer.mahmoodlab.org) to allow for further exploration and prognostic biomarker discovery. To validate that these model explanations are prognostic, we further analyzed high attention morphological regions in WSIs, which indicates that tumor-infiltrating lymphocyte presence corroborates with favorable cancer prognosis on 9 out of 14 cancer types studied. Comment: Demo: http://pancancer.mahmoodlab.org
Keywords	Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Artificial Intelligence ; Quantitative Biology - Genomics ; Quantitative Biology - Quantitative Methods ; Quantitative Biology - Tissues and Organs
Subject code	004
Publishing date	2021-08-04
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A functional genomics screen identifying blood cell development genes in Drosophila by undergraduates participating in a course-based research experience.

Evans, Cory J / Olson, John M / Mondal, Bama Charan / Kandimalla, Pratyush / Abbasi, Ariano / Abdusamad, Mai M / Acosta, Osvaldo / Ainsworth, Julia A / Akram, Haris M / Albert, Ralph B / Alegria-Leal, Elitzander / Alexander, Kai Y / Ayala, Angelica C / Balashova, Nataliya S / Barber, Rebecca M / Bassi, Harmanjit / Bennion, Sean P / Beyder, Miriam / Bhatt, Kush V /

Bhoot, Chinmay / Bradshaw, Aaron W / Brannigan, Tierney G / Cao, Boyu / Cashell, Yancey Y / Chai, Timothy / Chan, Alex W / Chan, Carissa / Chang, Inho / Chang, Jonathan / Chang, Michael T / Chang, Patrick W / Chang, Stephen / Chari, Neel / Chassiakos, Alexander J / Chen, Iris E / Chen, Vivian K / Chen, Zheying / Cheng, Marsha R / Chiang, Mimi / Chiu, Vivian / Choi, Sharon / Chung, Jun Ho / Contreras, Liset / Corona, Edgar / Cruz, Courtney J / Cruz, Renae L / Dang, Jefferson M / Dasari, Suhas P / De La Fuente, Justin R O / Del Rio, Oscar M A / Dennis, Emily R / Dertsakyan, Petros S / Dey, Ipsita / Distler, Rachel S / Dong, Zhiqiao / Dorman, Leah C / Douglass, Mark A / Ehresman, Allysen B / Fu, Ivy H / Fua, Andrea / Full, Sean M / Ghaffari-Rafi, Arash / Ghani, Asmar Abdul / Giap, Bosco / Gill, Sonia / Gill, Zafar S / Gills, Nicholas J / Godavarthi, Sindhuja / Golnazarian, Talin / Goyal, Raghav / Gray, Ricardo / Grunfeld, Alexander M / Gu, Kelly M / Gutierrez, Natalia C / Ha, An N / Hamid, Iman / Hanson, Ashley / Hao, Celesti / He, Chongbin / He, Mengshi / Hedtke, Joshua P / Hernandez, Ysrael K / Hlaing, Hnin / Hobby, Faith A / Hoi, Karen / Hope, Ashley C / Hosseinian, Sahra M / Hsu, Alice / Hsueh, Jennifer / Hu, Eileen / Hu, Spencer S / Huang, Stephanie / Huang, Wilson / Huynh, Melanie / Javier, Carmen / Jeon, Na Eun / Ji, Sunjong / Johal, Jasmin / John, Amala / Johnson, Lauren / Kadakia, Saurin / Kakade, Namrata / Kamel, Sarah / Kaur, Ravinder / Khatra, Jagteshwar S / Kho, Jeffrey A / Kim, Caleb / Kim, Emily Jin-Kyung / Kim, Hee Jong / Kim, Hyun Wook / Kim, Jin Hee / Kim, Seong Ah / Kim, Woo Kyeom / Kit, Brian / La, Cindy / Lai, Jonathan / Lam, Vivian / Le, Nguyen Khoi / Lee, Chi Ju / Lee, Dana / Lee, Dong Yeon / Lee, James / Lee, Jason / Lee, Jessica / Lee, Ju-Yeon / Lee, Sharon / Lee, Terrence C / Lee, Victoria / Li, Amber J / Li, Jialing / Libro, Alexandra M / Lien, Irvin C / Lim, Mia / Lin, Jeffrey M / Liu, Connie Y / Liu, Steven C / Louie, Irene / Lu, Shijia W / Luo, William Y / Luu, Tiffany / Madrigal, Josef T / Mai, Yishan / Miya, Darron I / Mohammadi, Mina / Mohanta, Sayonika / Mokwena, Tebogo / Montoya, Tonatiuh / Mould, Dallas L / Murata, Mark R / Muthaiya, Janani / Naicker, Seethim / Neebe, Mallory R / Ngo, Amy / Ngo, Duy Q / Ngo, Jamie A / Nguyen, Anh T / Nguyen, Huy C X / Nguyen, Rina H / Nguyen, Thao T T / Nguyen, Vincent T / Nishida, Kevin / Oh, Seo-Kyung / Omi, Kristen M / Onglatco, Mary C / Almazan, Guadalupe Ortega / Paguntalan, Jahzeel / Panchal, Maharshi / Pang, Stephanie / Parikh, Harin B / Patel, Purvi D / Patel, Trisha H / Petersen, Julia E / Pham, Steven / Phan-Everson, Tien M / Pokhriyal, Megha / Popovich, Davis W / Quaal, Adam T / Querubin, Karl / Resendiz, Anabel / Riabkova, Nadezhda / Rong, Fred / Salarkia, Sarah / Sama, Nateli / Sang, Elaine / Sanville, David A / Schoen, Emily R / Shen, Zhouyang / Siangchin, Ken / Sibal, Gabrielle / Sin, Garuem / Sjarif, Jasmine / Smith, Christopher J / Soeboer, Annisa N / Sosa, Cristian / Spitters, Derek / Stender, Bryan / Su, Chloe C / Summapund, Jenny / Sun, Beatrice J / Sutanto, Christine / Tan, Jaime S / Tan, Nguon L / Tangmatitam, Parich / Trac, Cindy K / Tran, Conny / Tran, Daniel / Tran, Duy / Tran, Vina / Truong, Patrick A / Tsai, Brandon L / Tsai, Pei-Hua / Tsui, C Kimberly / Uriu, Jackson K / Venkatesh, Sanan / Vo, Maique / Vo, Nhat-Thi / Vo, Phuong / Voros, Timothy C / Wan, Yuan / Wang, Eric / Wang, Jeffrey / Wang, Michael K / Wang, Yuxuan / Wei, Siman / Wilson, Matthew N / Wong, Daniel / Wu, Elliott / Xing, Hanning / Xu, Jason P / Yaftaly, Sahar / Yan, Kimberly / Yang, Evan / Yang, Rebecca / Yao, Tony / Yeo, Patricia / Yip, Vivian / Yogi, Puja / Young, Gloria Chin / Yung, Maggie M / Zai, Alexander / Zhang, Christine / Zhang, Xiao X / Zhao, Zijun / Zhou, Raymond / Zhou, Ziqi / Abutouk, Mona / Aguirre, Brian / Ao, Chon / Baranoff, Alexis / Beniwal, Angad / Cai, Zijie / Chan, Ryan / Chien, Kenneth Chang / Chaudhary, Umar / Chin, Patrick / Chowdhury, Praptee / Dalie, Jamlah / Du, Eric Y / Estrada, Alec / Feng, Erwin / Ghaly, Monica / Graf, Rose / Hernandez, Eduardo / Herrera, Kevin / Ho, Vivien W / Honeychurch, Kaitlyn / Hou, Yurianna / Huang, Jo M / Ishii, Momoko / James, Nicholas / Jang, Gah-Eun / Jin, Daphne / Juarez, Jesse / Kesaf, Ayse Elif / Khalsa, Sat Kartar / Kim, Hannah / Kovsky, Jenna / Kuang, Chak Lon / Kumar, Shraddha / Lam, Gloria / Lee, Ceejay / Lee, Grace / Li, Li / Lin, Joshua / Liu, Josephine / Ly, Janice / Ma, Austin / Markovic, Hannah / Medina, Cristian / Mungcal, Jonelle / Naranbaatar, Bilguudei / Patel, Kayla / Petersen, Lauren / Phan, Amanda / Phung, Malcolm / Priasti, Nadiyah / Ruano, Nancy / Salim, Tanveer / Schnell, Kristen / Shah, Paras / Shen, Jinhua / Stutzman, Nathan / Sukhina, Alisa / Tian, Rayna / Vega-Loza, Andrea / Wang, Joyce / Wang, Jun / Watanabe, Rina / Wei, Brandon / Xie, Lillian / Ye, Jessica / Zhao, Jeffrey / Zimmerman, Jill / Bracken, Colton / Capili, Jason / Char, Andrew / Chen, Michel / Huang, Pingdi / Ji, Sena / Kim, Emily / Kim, Kenneth / Ko, Julie / Laput, Sean Louise G / Law, Sam / Lee, Sang Kuk / Lee, Olivia / Lim, David / Lin, Eric / Marik, Kyle / Mytych, Josh / O'Laughlin, Andie / Pak, Jensen / Park, Claire / Ryu, Ruth / Shinde, Ashwin / Sosa, Manny / Waite, Nick / Williams, Mane / Wong, Richard / Woo, Jocelyn / Woo, Jonathan / Yepuri, Vishaal / Yim, Dorothy / Huynh, Dan / Wijiewarnasurya, Dinali / Shapiro, Casey / Levis-Fitzgerald, Marc / Jaworski, Leslie / Lopatto, David / Clark, Ira E / Johnson, Tracy / Banerjee, Utpal

G3 (Bethesda, Md.)

2021 Volume 11, Issue 1

Abstract: Undergraduate students participating in the UCLA Undergraduate Research Consortium for Functional Genomics (URCFG) have conducted a two-phased screen using RNA interference (RNAi) in combination with fluorescent reporter proteins to identify genes ... ...

Abstract	Undergraduate students participating in the UCLA Undergraduate Research Consortium for Functional Genomics (URCFG) have conducted a two-phased screen using RNA interference (RNAi) in combination with fluorescent reporter proteins to identify genes important for hematopoiesis in Drosophila. This screen disrupted the function of approximately 3500 genes and identified 137 candidate genes for which loss of function leads to observable changes in the hematopoietic development. Targeting RNAi to maturing, progenitor, and regulatory cell types identified key subsets that either limit or promote blood cell maturation. Bioinformatic analysis reveals gene enrichment in several previously uncharacterized areas, including RNA processing and export and vesicular trafficking. Lastly, the participation of students in this course-based undergraduate research experience (CURE) correlated with increased learning gains across several areas, as well as increased STEM retention, indicating that authentic, student-driven research in the form of a CURE represents an impactful and enriching pedagogical approach.
MeSH term(s)	Animals ; Blood Cells ; Drosophila/genetics ; Genomics/education ; Humans ; Students ; Universities
Language	English
Publishing date	2021-02-12
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2629978-1
ISSN	2160-1836 ; 2160-1836
ISSN (online)	2160-1836
ISSN	2160-1836
DOI	10.1093/g3journal/jkaa028
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