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14 results on '"Williams, Mane"'

1. A General-Purpose Self-Supervised Model for Computational Pathology

Author

Chen, Richard J., Ding, Tong, Lu, Ming Y., Williamson, Drew F. K., Jaume, Guillaume, Chen, Bowen, Zhang, Andrew, Shao, Daniel, Song, Andrew H., Shaban, Muhammad, Williams, Mane, Vaidya, Anurag, Sahai, Sharifa, Oldenburg, Lukas, Weishaupt, Luca L., Wang, Judy J., Williams, Walt, Le, Long Phi, Gerber, Georg, and Mahmood, Faisal

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Quantitative Biology - Tissues and Organs
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.

Published
2023

2. Weakly Supervised AI for Efficient Analysis of 3D Pathology Samples

Author

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., and Mahmood, Faisal

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Quantitative Biology - Quantitative Methods
Abstract

Human tissue and its constituent cells form a microenvironment that is fundamentally three-dimensional (3D). However, the standard-of-care in pathologic diagnosis involves selecting a few two-dimensional (2D) sections for microscopic evaluation, risking sampling bias and misdiagnosis. Diverse methods for capturing 3D tissue morphologies have been developed, but they have yet had little translation to clinical practice; manual and computational evaluations of such large 3D data have so far been impractical and/or unable to provide patient-level clinical insights. Here we present Modality-Agnostic Multiple instance learning for volumetric Block Analysis (MAMBA), a deep-learning-based platform for processing 3D tissue images from diverse imaging modalities and predicting patient outcomes. Archived prostate cancer specimens were imaged with open-top light-sheet microscopy or microcomputed tomography and the resulting 3D datasets were used to train risk-stratification networks based on 5-year biochemical recurrence outcomes via MAMBA. With the 3D block-based approach, MAMBA achieves an area under the receiver operating characteristic curve (AUC) of 0.86 and 0.74, superior to 2D traditional single-slice-based prognostication (AUC of 0.79 and 0.57), suggesting superior prognostication with 3D morphological features. Further analyses reveal that the incorporation of greater tissue volume improves prognostic performance and mitigates risk prediction variability from sampling bias, suggesting the value of capturing larger extents of heterogeneous 3D morphology. With the rapid growth and adoption of 3D spatial biology and pathology techniques by researchers and clinicians, MAMBA provides a general and efficient framework for 3D weakly supervised learning for clinical decision support and can help to reveal novel 3D morphological biomarkers for prognosis and therapeutic response.

Published
2023

3. Towards a general-purpose foundation model for computational pathology

Author

Chen, Richard J., Ding, Tong, Lu, Ming Y., Williamson, Drew F. K., Jaume, Guillaume, Song, Andrew H., Chen, Bowen, Zhang, Andrew, Shao, Daniel, Shaban, Muhammad, Williams, Mane, Oldenburg, Lukas, Weishaupt, Luca L., Wang, Judy J., Vaidya, Anurag, Le, Long Phi, Gerber, Georg, Sahai, Sharifa, Williams, Walt, and Mahmood, Faisal

Published
2024
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4. Pan-Cancer Integrative Histology-Genomic Analysis via Interpretable Multimodal Deep Learning

Author

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, and Mahmood, Faisal

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Quantitative Biology - Genomics, Quantitative Biology - Quantitative Methods, Quantitative Biology - Tissues and Organs
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

Published
2021

5. Analysis of 3D pathology samples using weakly supervised AI

Author

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., and Mahmood, Faisal

Published
2024
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10. INTERNATIONAL EVALUATION OF WEAKLY-SUPERVISED AI-MODEL FOR CARDIAC ALLOGRAFT REJECTION SCREENING

Author

Lipkova, Jana, primary, Chen, Tiffany Y., additional, Lu, Ming Y., additional, Shady, Maha, additional, Williams, Mane, additional, Wang, Jingwen, additional, Mitchell, Richard N., additional, Turan, Mehmet, additional, Coskun, Gulfize, additional, Demir, Derya, additional, Nart, Deniz, additional, Barbet, Funda Y., additional, Banz, Yara, additional, Odening, Katja E., additional, and Mahmood, Faisal, additional

Published
2021
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11. A functional genomics screen identifying blood cell development genes in Drosophila by undergraduates participating in a course-based research experience

Author

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

Published
2021
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12. A General-Purpose Self-Supervised Model for Computational Pathology.

Author

Chen RJ, Ding T, Lu MY, Williamson DFK, Jaume G, Chen B, Zhang A, Shao D, Song AH, Shaban M, Williams M, Vaidya A, Sahai S, Oldenburg L, Weishaupt LL, Wang JJ, Williams W, Le LP, Gerber G, and Mahmood F

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.

Published
2023

13. Weakly Supervised AI for Efficient Analysis of 3D Pathology Samples.

Author

Song AH, Williams M, Williamson DFK, Jaume G, Zhang A, Chen B, Serafin R, Liu JTC, Baras A, Parwani AV, and Mahmood F

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 1,2 , with concomitant risks of sampling bias and misdiagnosis 3-6 . To this end, there have been intense efforts to capture 3D tissue morphology and transition to 3D pathology, with the development of multiple high-resolution 3D imaging modalities 7-18 . However, these tools have had little translation to clinical practice as manual evaluation of such large data by pathologists is impractical and there is a lack of computational platforms that can efficiently process the 3D images and provide patient-level clinical insights. Here we present Modality-Agnostic Multiple instance learning for volumetric Block Analysis (MAMBA), a deep-learning-based platform for processing 3D tissue images from diverse imaging modalities and predicting patient outcomes. Archived prostate cancer specimens were imaged with open-top light-sheet microscopy 12-14 or microcomputed tomography 15,16 and the resulting 3D datasets were used to train risk-stratification networks based on 5-year biochemical recurrence outcomes via MAMBA. With the 3D block-based approach, MAMBA achieves an area under the receiver operating characteristic curve (AUC) of 0.86 and 0.74, superior to 2D traditional single-slice-based prognostication (AUC of 0.79 and 0.57), suggesting superior prognostication with 3D morphological features. Further analyses reveal that the incorporation of greater tissue volume improves prognostic performance and mitigates risk prediction variability from sampling bias, suggesting that there is value in capturing larger extents of spatially heterogeneous 3D morphology. With the rapid growth and adoption of 3D spatial biology and pathology techniques by researchers and clinicians, MAMBA provides a general and efficient framework for 3D weakly supervised learning for clinical decision support and can help to reveal novel 3D morphological biomarkers for prognosis and therapeutic response., Competing Interests: Competing Interests J.T.C.L. is a co-founder and board member of Alpenglow Biosciences, Inc., which has licensed the OTLS microscopy portfolio developed in his lab at the University of Washington.

Published
2023

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

Author

Evans CJ, Olson JM, Mondal BC, Kandimalla P, Abbasi A, Abdusamad MM, Acosta O, Ainsworth JA, Akram HM, Albert RB, Alegria-Leal E, Alexander KY, Ayala AC, Balashova NS, Barber RM, Bassi H, Bennion SP, Beyder M, Bhatt KV, Bhoot C, Bradshaw AW, Brannigan TG, Cao B, Cashell YY, Chai T, Chan AW, Chan C, Chang I, Chang J, Chang MT, Chang PW, Chang S, Chari N, Chassiakos AJ, Chen IE, Chen VK, Chen Z, Cheng MR, Chiang M, Chiu V, Choi S, Chung JH, Contreras L, Corona E, Cruz CJ, Cruz RL, Dang JM, Dasari SP, De La Fuente JRO, Del Rio OMA, Dennis ER, Dertsakyan PS, Dey I, Distler RS, Dong Z, Dorman LC, Douglass MA, Ehresman AB, Fu IH, Fua A, Full SM, Ghaffari-Rafi A, Ghani AA, Giap B, Gill S, Gill ZS, Gills NJ, Godavarthi S, Golnazarian T, Goyal R, Gray R, Grunfeld AM, Gu KM, Gutierrez NC, Ha AN, Hamid I, Hanson A, Hao C, He C, He M, Hedtke JP, Hernandez YK, Hlaing H, Hobby FA, Hoi K, Hope AC, Hosseinian SM, Hsu A, Hsueh J, Hu E, Hu SS, Huang S, Huang W, Huynh M, Javier C, Jeon NE, Ji S, Johal J, John A, Johnson L, Kadakia S, Kakade N, Kamel S, Kaur R, Khatra JS, Kho JA, Kim C, Kim EJ, Kim HJ, Kim HW, Kim JH, Kim SA, Kim WK, Kit B, La C, Lai J, Lam V, Le NK, Lee CJ, Lee D, Lee DY, Lee J, Lee J, Lee J, Lee JY, Lee S, Lee TC, Lee V, Li AJ, Li J, Libro AM, Lien IC, Lim M, Lin JM, Liu CY, Liu SC, Louie I, Lu SW, Luo WY, Luu T, Madrigal JT, Mai Y, Miya DI, Mohammadi M, Mohanta S, Mokwena T, Montoya T, Mould DL, Murata MR, Muthaiya J, Naicker S, Neebe MR, Ngo A, Ngo DQ, Ngo JA, Nguyen AT, Nguyen HCX, Nguyen RH, Nguyen TTT, Nguyen VT, Nishida K, Oh SK, Omi KM, Onglatco MC, Almazan GO, Paguntalan J, Panchal M, Pang S, Parikh HB, Patel PD, Patel TH, Petersen JE, Pham S, Phan-Everson TM, Pokhriyal M, Popovich DW, Quaal AT, Querubin K, Resendiz A, Riabkova N, Rong F, Salarkia S, Sama N, Sang E, Sanville DA, Schoen ER, Shen Z, Siangchin K, Sibal G, Sin G, Sjarif J, Smith CJ, Soeboer AN, Sosa C, Spitters D, Stender B, Su CC, Summapund J, Sun BJ, Sutanto C, Tan JS, Tan NL, Tangmatitam P, Trac CK, Tran C, Tran D, Tran D, Tran V, Truong PA, Tsai BL, Tsai PH, Tsui CK, Uriu JK, Venkatesh S, Vo M, Vo NT, Vo P, Voros TC, Wan Y, Wang E, Wang J, Wang MK, Wang Y, Wei S, Wilson MN, Wong D, Wu E, Xing H, Xu JP, Yaftaly S, Yan K, Yang E, Yang R, Yao T, Yeo P, Yip V, Yogi P, Young GC, Yung MM, Zai A, Zhang C, Zhang XX, Zhao Z, Zhou R, Zhou Z, Abutouk M, Aguirre B, Ao C, Baranoff A, Beniwal A, Cai Z, Chan R, Chien KC, Chaudhary U, Chin P, Chowdhury P, Dalie J, Du EY, Estrada A, Feng E, Ghaly M, Graf R, Hernandez E, Herrera K, Ho VW, Honeychurch K, Hou Y, Huang JM, Ishii M, James N, Jang GE, Jin D, Juarez J, Kesaf AE, Khalsa SK, Kim H, Kovsky J, Kuang CL, Kumar S, Lam G, Lee C, Lee G, Li L, Lin J, Liu J, Ly J, Ma A, Markovic H, Medina C, Mungcal J, Naranbaatar B, Patel K, Petersen L, Phan A, Phung M, Priasti N, Ruano N, Salim T, Schnell K, Shah P, Shen J, Stutzman N, Sukhina A, Tian R, Vega-Loza A, Wang J, Wang J, Watanabe R, Wei B, Xie L, Ye J, Zhao J, Zimmerman J, Bracken C, Capili J, Char A, Chen M, Huang P, Ji S, Kim E, Kim K, Ko J, Laput SLG, Law S, Lee SK, Lee O, Lim D, Lin E, Marik K, Mytych J, O'Laughlin A, Pak J, Park C, Ryu R, Shinde A, Sosa M, Waite N, Williams M, Wong R, Woo J, Woo J, Yepuri V, Yim D, Huynh D, Wijiewarnasurya D, Shapiro C, Levis-Fitzgerald M, Jaworski L, Lopatto D, Clark IE, Johnson T, and Banerjee U

Subjects
Animals, Blood Cells, Humans, Students, Drosophila genetics, Genomics education, Universities
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., (© The Author(s) 2021. Published by Oxford University Press on behalf of Genetics Society of America.)

Published
2021
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