Joshua D. Campbell, PhD
Associate Professor, Medicine
Biography
Computational biology and bioinformatics.
High-throughput genomic technologies are rapidly evolving including the areas of DNA and RNA sequencing. Novel types of complex data are being rapidly generated and require novel methods for quality control and analysis. We are currently focused on developing and/or applying methods for identifying genomic alterations in cancer, quantifying the mutagenic effect of carcinogens, and characterizing cellular heterogeneity using single cell RNA sequencing. We are applying these methods in the areas of lung cancer development and premalignancy as well as COPD pathogenesis as described below.
Identifying early drivers of lung cancer.
Lung adenocarcinomas and lung squamous cell carcinomas are the most common types of lung cancer and remain major causes of death worldwide despite advances in smoking cessation, early detection, and targeted and immunological therapies. Many patients have lung cancers that do not harbor a known activating mutation and therefore cannot be given targeted therapies. In collaboration with labs from Dana-Farber Cancer Institute, the Broad Institute, and The Cancer Genome Atlas (TCGA) consortium, we analyze next-generation sequencing data to identify novel drivers of lung tumorigenesis. Targeting these genes with novel therapies will hopefully lead to a reduction in overall lung cancer mortality. In collaboration with the Spira/Lenburg lab at BUSM, we are identifying the genomic alterations in premalignant lesions for squamous cell carcinoma with the ultimate goal of defining strategies for early detection.
Therapeutic development and pathogenesis of COPD.
Chronic Obstructive Pulmonary Disease (COPD) is the 4th leading cause of death in the world. Our understanding of the molecular mechanisms responsible for the initiation and progression of this disease are limited. By examining expression differences between individuals with and without COPD or differences within a person along a gradient of disease, we hope to elucidate the molecular mechanisms that responsible for disease initiation. Utilizing publicly available resources such as the Connectivity Map, we are also using gene expression data to predict novel therapeutics for the treatment of COPD.
Other Positions
- Member, BU-BMC Cancer Center, Boston University
- Member, Evans Center for Interdisciplinary Biomedical Research, Boston University
- Member, Genome Science Institute, Boston University
Education
- Boston University, PhD
- Anderson University, BS
Publications
- Published on 8/1/2024
Sarfraz I, Wang Y, Shastry A, Teh WK, Sokolov A, Herb BR, Creasy HH, Virshup I, Dries R, Degatano K, Mahurkar A, Schnell DJ, Madrigal P, Hilton J, Gehlenborg N, Tickle T, Campbell JD. MAMS: matrix and analysis metadata standards to facilitate harmonization and reproducibility of single-cell data. Genome Biol. 2024 Aug 01; 25(1):205. PMID: 39090672.
Read at: PubMed - Published on 7/31/2024
Sun Y, Benmhammed H, Al Abdullatif S, Habara A, Fu E, Brady J, Williams C, Ilinski A, Sharma A, Mahdaviani K, Alekseyev YO, Campbell JD, Steinberg MH, Cui S. PGC-1a agonism induces fetal hemoglobin and exerts antisickling effects in sickle cell disease. Sci Adv. 2024 Aug 02; 10(31):eadn8750. PMID: 39083598.
Read at: PubMed - Published on 1/11/2024
Yin Y, Yajima M, Campbell JD. Characterization and decontamination of background noise in droplet-based single-cell protein expression data with DecontPro. Nucleic Acids Res. 2024 Jan 11; 52(1):e4. PMID: 37973397.
Read at: PubMed - Published on 11/4/2023
Chevalier A, Guo T, Gurevich NQ, Xu J, Yajima M, Campbell JD. Characterization of highly active mutational signatures in tumors from a large Chinese population. medRxiv. 2023 Nov 04. PMID: 37961450.
Read at: PubMed - Published on 10/2/2023
Yanagawa J, Tran LM, Salehi-Rad R, Lim RJ, Dumitras C, Fung E, Wallace WD, Prosper AE, Fishbein G, Shea C, Hong R, Kahangi B, Deng JJ, Gower AC, Liu B, Campbell JD, Mazzilli SA, Beane JE, Kadara H, Lenburg ME, Spira AE, Aberle DR, Krysan K, Dubinett SM. Single-Cell Characterization of Pulmonary Nodules Implicates Suppression of Immunosurveillance across Early Stages of Lung Adenocarcinoma. Cancer Res. 2023 Oct 02; 83(19):3305-3319. PMID: 37477508.
Read at: PubMed - Published on 9/19/2023
O'Neill NK, Stein TD, Hu J, Rehman H, Campbell JD, Yajima M, Zhang X, Farrer LA. Bulk brain tissue cell-type deconvolution with bias correction for single-nuclei RNA sequencing data using DeTREM. BMC Bioinformatics. 2023 Sep 19; 24(1):349. PMID: 37726653.
Read at: PubMed - Published on 8/11/2023
Pavel AB, Garrison C, Luo L, Liu G, Taub D, Xiao J, Juan-Guardela B, Tedrow J, Alekseyev YO, Yang IV, Geraci MW, Sciurba F, Schwartz DA, Kaminski N, Beane J, Spira A, Lenburg ME, Campbell JD. Integrative genetic and genomic networks identify microRNA associated with COPD and ILD. Sci Rep. 2023 Aug 11; 13(1):13076. PMID: 37567908.
Read at: PubMed - Published on 8/3/2023
Wang Y, Sarfraz I, Pervaiz N, Hong R, Koga Y, Akavoor V, Cao X, Alabdullatif S, Zaib SA, Wang Z, Jansen F, Yajima M, Johnson WE, Campbell JD. Interactive analysis of single-cell data using flexible workflows with SCTK2. Patterns (N Y). 2023 Aug 11; 4(8):100814. PMID: 37602214.
Read at: PubMed - Published on 6/29/2023
Arceneaux D, Chen Z, Simmons AJ, Heiser CN, Southard-Smith AN, Brenan MJ, Yang Y, Chen B, Xu Y, Choi E, Campbell JD, Liu Q, Lau KS. A contamination focused approach for optimizing the single-cell RNA-seq experiment. iScience. 2023 Jul 21; 26(7):107242. PMID: 37496679.
Read at: PubMed - Published on 2/24/2023
Yin Y, Yajima M, Campbell JD. Characterization and decontamination of background noise in droplet-based single-cell protein expression data with DecontPro. bioRxiv. 2023 Feb 24. PMID: 36865227.
Read at: PubMed
View 52 more publications: View full profile at BUMC