Biology subjects

Parker, S. J.

Publications and source records attributed to Parker, S. J..

16 recordsLinked to original sources

bioRxiv2026

Protocol for Single-Cell Patch-Proteomics: identify specific proteins associated with electrophysiological properties from individual neurons

Single-cell patch-proteomics is a novel and powerful technique combining electrophysiological and quantitative proteomic characterization from individually sampled neurons. Following whole-cell patch clamp characterization of neuronal excitability, we collect intracellular contents and, using the latest advancements in Liquid Chromatography and Mass Spectrometry, identify and quantify peptides and their corresponding proteins. By cell-matched one-to-one associations between electrophysiological properties and protein abundance, this approach provides a means to elucidate molecular mechanisms of neuronal function on individual neuron basis.

neuroscience↗

bioRxiv2026

Endogenous insulin mediates dynamic coordination between pancreatic cancer and systemic metabolism

Pancreatic ductal adenocarcinoma (PDAC) is commonly associated with obesity, diabetes, and cachexia. The pancreatic anabolic hormone, insulin, is implicated in each of these metabolic diseases, but how insulin levels affect tumor growth and relevant host physiological factors, was unclear. To address this, we transplanted orthotopic PDAC patient-derived organoids into mice consuming a hyperinsulinemia-inducing high-fat diet (HFD). We found that insulin concentrations were higher in tumors compared to circulation. Genetically reducing insulin levels reduced PDAC growth, particularly in HFD-fed mature male mice. In turn, we found the presence of pancreatic tumors increased glucose clearance and limited the expected dietary-induced gains in insulin, weight and fat mass. Interestingly, tumor growth in normal-chow-fed mice, but not HFD-fed mice, was associated with declining circulating insulin levels, as well as declines in fat and muscle mass. Together, these data provide new insights into the complex, insulin-centred interplay between PDAC and the hosts systemic metabolism that underlie cancer-associated metabolic dysfunction.

cancer biology↗

bioRxiv2026

Asporin Improves Cardiac Myocyte Response to Ischemia and Reperfusion Stress

BackgroundMyocardial Infarction (MI) remains a leading cause of mortality worldwide, despite advancements in clinical therapies and interventions. MI results from prolonged ischemia, leading to hypoxia-induced damage to cardiac tissue and reperfusion-injury (R/I) that aggravates cardiomyocyte (CM) loss. One key cellular event during this process is accumulation of dysfunctional mitochondria, resulting from environmental hypoxia and subsequent oxidative stress upon reperfusion. Post-MI cardiac-remodeling involves changes in both cellular and extracellular matrix (ECM). Ubiquitin-dependent and independent autophagy are crucial for cardio protection during this phase. The ECM provides structural integrity and functions as a reservoir for signaling molecules. Asporin (ASPN), a small leucine-rich proteoglycan, plays a role in modulating cardiac-remodeling by limiting excessive fibrosis and protecting CMs from cell death. MethodsWe investigated the therapeutic potential of ASPN by using an exogenous recombinant peptide of ASPN (rASPN), testing its effects using an in-vitro ischemia-reperfusion (I/R) model simulating MI conditions. Two I/R models were developed using an immortalized human embryonic cardiac cell line to reflect the hypoxia-reperfusion (H/R) phases of MI. In the No-Reoxygenation (No-ReOx) model, cells were subjected to hypoxia for 18 hours, with or without exogenous rASPN. In the Reoxygenation (ReOx) model, cells underwent 18 hours of hypoxia, then 12 hours of reoxygenation (simulating reperfusion), with or without rASPN. ResultsProteomics revealed that ASPN modulates key pathways involved in apoptosis, non-canonical autophagy, and metabolic reprogramming. Additionally, ASPN influenced immune response pathways and significantly affected TGF-{beta} signaling, a central mediator of cardiac fibrosis and remodeling post-MI. These findings indicate that ASPN plays a multifaceted role in regulating cellular responses to hypoxia and R/I. ConclusionsOur H/R model simulates key aspects of MI and R/I. The protective role of ASPN observed in this model suggests it as a promising candidate for developing cardioprotective therapies to minimize R/I and adverse cardiac-remodeling following MI.

physiology↗

bioRxiv2025

The mutational landscape defines the proteome and spatial organization of tumor, stroma and immune cells in ovarian cancer

High-grade serous ovarian cancer (HGSOC) is a highly aggressive and lethal form of ovarian cancer. Challenges to diagnosis and treatment include a lack of effective screening methods for early detection, the absence of cancer-specific symptoms, and the development of chemoresistance. The genomic instability of HGSOC, further complicated by homologous recombination deficiency (HRD), leads to heterogeneity in HGSOC tumors and patient response to treatment. This makes it challenging to develop a single, effective diagnostic and treatment approach for this disease. Proteogenomic studies have provided some insight into HGSOC biology, but a deeper understanding of the tumor proteome through chemotherapy and disease recurrence is needed. Here we have profiled the proteome of tumors from 29 HGSOC patients before and after multiple rounds of chemotherapy. The proteome of HGSOC tumors remained unchanged for individual patients even after numerous rounds of chemotherapy. Differential expression analysis revealed known and novel proteins associated with chemoresistance and HRD status, further supported by similar changes at the genetic and epigenetic levels. We found that HRD affected proteins related to immune pathways. HRD was also associated with more shared T cell receptor (TCR) CDR3 repertoires of tumor-infiltrating T cells and increased neoantigen counts. ERAP1, a protein involved in peptide trimming before antigen presentation to immune cells via MHC-class I, was overexpressed in homologous recombination proficient (HRP) tumors and negatively correlated to neoantigen count. Its potential role in immune suppression in HRP tumors makes it an attractive therapeutic target that may be effective in combination with immunotherapy, particularly for HRP tumors. 26-plex immunostaining of HGSOC whole tissues further revealed significant differences in the spatial proximities of immune cells to each other and to tumor cells based on HRD status. Through proteomic and imaging analysis, this work has shown that the immune landscape of HGSOC tumors is influenced by homologous recombination status and identified candidate drivers of HGSOC biology.

cancer biology↗

bioRxiv2025

Single Cell Proteomics Reveals Novel Cell Phenotypes in Marfan Mouse Aneurysm

BackgroundSingle-cell omics technology is a powerful tool in biomedical research. However, single cell proteomics has lagged due to an inability to amplify peptides in a similar fashion to nucleotide strings. Single cell proteomics is important because proteins are the main functional unit in cells, and they often poorly correlate with mRNA quantities. In this paper we describe the first single cell proteomic analysis of complex tissue, comparing aneurysmal and normal mouse aorta from males and females. We also compare and integrate our single cell proteomic profiles with a matching single cell transcriptomics dataset. MethodsWe compared single cell proteomes between male and female, wild-type and Fbn1C1041G/+ Marfan mice (N=3 per group). Individual cells from mouse aortic root single cell suspensions were deposited in 384 well plates and subjected to ultra-sensitive nanoflow liquid chromatography-ion mobility-time of flight-mass spectrometry. The data were analyzed with leiden clustering to identify cell types. Statistical analyses were performed to detect differential proteins within cell types and multi-omics analysis integrated single cell proteomics with published single cell RNA-seq. ResultsWe identified all major aortic cell types including 7 distinct smooth muscle cell subtypes. The proportion of these cells varied based on sex and the Fbn1C1041G/+ genotype. Differentially expressed proteins between male and female in addition to wild-type and Marfan samples uncovered enhanced endothelial to mesenchymal transition patterns in endothelial cells from male Marfan mice. Comparisons between single cell RNA and single cell proteomic profiles showed similarities in major subtypes but not smooth muscle cell subtypes. Multi-omics analysis of these two single cell platforms demonstrated a potential novel role for smooth muscle cell derived angiotensin signaling in the Marfan phenotype. ConclusionsSingle cell proteomics identified new subpopulations of vascular smooth muscles cells and novel cell type specific protein signatures related to sex differences and aneurysm formation. AbbreviationsNext generation sequencing (NGS), Mass spectrometer (MS), Single cell proteomics by Mass Spectrometry (ScOPE-MS), Marfans syndrome (MFS), Fibrillin 1 (FBN1), Transforming growth factor {beta} (TGF{beta}), Smooth muscle cell (SMC), Single cell proteomic (scProteomic), Differentially expressed proteins (DEPs), Wild-type (WT), Hanks balanced salt solution (HBSS), Fetal bovine serum (FBS), Dulbeccos Modified Eagle Medium (DMEM), Data-independent acquisition parallel accumulation-serial fragmentation (DIA-PASEF), Magnetic assisted cell sorted (MACS), Single Cell Analysis in Python (Scanpy), Kyoto Encyclopedia of Genes and Genomes (KEGG), Principal component analysis (PCA), Uniform manifold projection (UMAP), Single cell transcriptomic (scTranscriptomic), Smoothelin (Smtn), Transgelin (Tagln), Myosin heavy chain 11 (Myh11), Platelet endothelial cell adhesion molecule 1 (Pecam1), Dipeptidase 1 (Dpep1), Uncoupling protein 1 (Ucp1), Low-density lipoprotein receptor-related protein (Lrp1), DNA ligase 3 (Lig3), Capsaicin channel transient receptor potential vanilloid 1 (Trpv1), Endothelial to mesenchymal transition (endMT), Intercellular adhesion molecule 1 (Icam1), Intercellular adhesion molecule 2 (Icam2), Endothelial cell-selective adhesion molecule (Esam), Calponin 1 (Cnn1), Vimentin (Vim), Zinc finger E-box-binding homeobox 1 (Zeb1), Snail family transcriptional repressor 1 (Snai1), Tropomyosin alpha-4 chain (Tpm4), Angiotensin converting enzyme (Ace)

systems biology↗

bioRxiv2025

The mutational and clonality profile of HGSOC is established early in tumor development and conserved throughout therapy resistance

High grade serous ovarian cancer (HGSOC) is the most lethal gynecologic malignancy, killing more than 9,000 women each year in the United States alone. Nearly 80% of patients with HGSOC tumors will experience recurrence within 5 years, but little is known about the mechanisms that drive this process. Intratumor heterogeneity is believed to be a key feature of recurrence and resistance in HGSOC tumors, with early studies reporting diverse and complex mechanisms of the seeding of metastatic sites, including metastasis reseeding the primary tumor. Few studies have investigated temporal changes to clonality and structural variants through disease recurrence and the development of chemoresistance as surgical debulking is not frequently performed at this later stage. We performed multi-omic profiling in paired chemo-naive and chemoresistant tumors from 32 HGSOC patients to investigate the mutational and genomic landscape of disease progression. Somatic mutation profiles were largely conserved through disease progression. Mutational burdens did not significantly differ across recurrence but were driven by homologous recombination repair deficiency status. Clonal composition and dynamics were measured through variant allele frequency alterations as tumors progressed from primary chemo-naive to recurrent chemo-resistant tumors. A novel candidate driver gene, MDC1, from the homologous recombination repair pathway, was significantly mutated and over-represented in patients with homologous recombination proficient tumors, with somatic mutations clustered in a single exon. Tumor evolution and phylogeny revealed that few changes in clonal abundance and complexity occur across the disease course in these patients. Taking structural variants into account, homologous recombination repair proficient (HRP) tumors tend to be polyclonal while homologous recombination repair deficient (HRD) tumors tend to be monoclonal, accompanied by a longer progression-free survival than the HRP patients. Three distinct classes of tumors were identified by structural variant signature analysis: tumors defined by DNA losses, tumors defined by DNA gains, and tumors defined by copy number neutral changes, which were largely defined by HRD status. Each class displayed distinct regions of the chromosome that were frequently affected by large scale SV events (>5Mb). Although no regions were frequently altered in recurrent tumors, GO analysis revealed that recurrent tumors have a significantly reduced immune response, which was not seen in the primary tumors. Ultra long read sequencing validated a majority of the SVs identified in short read sequencing and identified additional SVs undetected by short reads. These analyses identify that the phenotype of high grade serous ovarian tumors as defined by mutation and clonality profiles is established early in disease development and remain largely unchanged through chemotherapy and recurrence. This, when considered with the significant inter-patient heterogeneity identified in HGSOC, demonstrates the need for personalized therapies based on tumor profiling.

genomics↗

bioRxiv2024

Identification of Disease-relevant, Sex-based Proteomic Differences in iPSC-derived Vascular Smooth Muscle

The prevalence of cardiovascular disease varies with sex, and the impact of intrinsic sex-based differences on vasculature is not well understood. Animal models can provide important insight into some aspects of human biology, however not all discoveries in animal systems translate well to humans. To explore the impact of chromosomal sex on proteomic phenotypes, we used iPSC-derived vascular smooth muscle cells from healthy donors of both sexes to identify sex-based proteomic differences and their possible effects on cardiovascular pathophysiology. Our analysis confirmed that differentiated cells have a proteomic profile more similar to healthy primary aortic smooth muscle than iPSCs. We also identified sex-based differences in iPSC- derived vascular smooth muscle in pathways related to ATP binding, glycogen metabolic process, and cadherin binding as well as multiple proteins relevant to cardiovascular pathophysiology and disease. Additionally, we explored the role of autosomal and sex chromosomes in protein regulation, identifying that proteins on autosomal chromosomes also show sex-based regulation that may affect the protein expression of proteins from autosomal chromosomes. This work supports the biological relevance of iPSC-derived vascular smooth muscle cells as a model for disease, and further exploration of the pathways identified here can lead to the discovery of sex-specific pharmacological targets for cardiovascular disease. SignificanceIn this work, we have differentiated 4 male and 4 female iPSC lines into vascular smooth muscle cells, giving us the ability to identify statistically-significant sex-specific proteomic markers that are relevant to cardiovascular disease risk (such as PCK2, MTOR, IGFBP2, PTGR2, and SULTE1).

molecular biology↗

bioRxiv2024

Cell Storage Conditions Impact Single-Cell Proteomic Landscapes

Single-cell transcriptomics (SCT) has revolutionized our understanding of cellular heterogeneity, yet the emergence of single-cell proteomics (SCP) promises a more functional view of cellular dynamics. A challenge is that not all mass spectrometry facilities can perform SCP, and not all labs have access to cell sorting equipment required for SCP, which together motivate an interest in sending bulk cell samples through the mail for sorting and SCP analysis. Shipping requires cell storage, which has an unknown impact on SCP results. This study investigates the impact of cell storage conditions on the proteomic landscape at the single-cell level utilizing a Data-Independent Acquisition (DIA) coupled with Parallel Accumulation Serial Fragmentation (diaPASEF). Three storage conditions were compared in 293T cells: (1) 37{degrees}C (control), (2) 4{degrees}C overnight, and (3) - 80{degrees}C storage followed by liquid nitrogen preservation. Either cold or frozen storage induced significant alterations in cell diameter, elongation, and proteome composition. By elucidating how cell storage conditions alter cellular morphology and proteome profiles, this study contributes foundational technical information about SCP sample preparation and data quality.

cell biology↗

bioRxiv2023

ABDS: tool suite for analyzing biologically diverse samples

MotivationAnalytics tools are essential to identify informative molecular features about different phenotypic groups. Among the most fundamental tasks are missing value imputation, signature gene detection, and expression pattern visualization. However, most commonly used analytics tools may be problematic for characterizing biologically diverse samples when either signature genes possess uneven missing rates across different groups yet involving complex missing mechanisms, or multiple biological groups are simultaneously compared and visualized. ResultsWe develop ABDS tool suite tailored specifically to analyzing biologically diverse samples. Mechanism-integrated group-wise imputation is developed to recruit signature genes involving informative missingness, cosine-based one-sample test is extended to detect enumerated signature genes, and unified heatmap is designed to comparably display complex expression patterns. We discuss the methodological principles and demonstrate the conceptual advantages of the three software tools. We also showcase the biomedical applications of these individual tools. Implemented in open-source R scripts, ABDS tool suite complements rather than replaces the existing tools and will allow biologists to more accurately detect interpretable molecular signals among diverse phenotypic samples. Availability and implementationThe R Scripts of ABDS tool suite is freely available at https://github.com/niccolodpdu/ABDS. Contactyuewang@vt.edu Supplementary informationSupplementary materials are available at Bioinformatics Advances online.

bioinformatics↗

bioRxiv2023

Complete Workflow for High Throughput Human Single Skeletal Muscle Fiber Proteomics

Skeletal muscle is a major regulatory tissue of whole-body metabolism and is composed of a diverse mixture of cell (fiber) types. Aging and several diseases differentially affect the various fiber types, and therefore, investigating the changes in the proteome in a fiber-type specific manner is essential. Recent breakthroughs in isolated single muscle fiber proteomics have started to reveal heterogeneity among fibers. However, existing procedures are slow and laborious requiring two hours of mass spectrometry time per single muscle fiber; 50 fibers would take approximately four days to analyze. Thus, to capture the high variability in fibers both within and between individuals requires advancements in high throughput single muscle fiber proteomics. Here we use a single cell proteomics method to enable quantification of single muscle fiber proteomes in 15 minutes total instrument time. As proof of concept, we present data from 53 isolated skeletal muscle fibers obtained from two healthy individuals analyzed in 13.25 hours. Adapting single cell data analysis techniques to integrate the data, we can reliably separate type 1 and 2A fibers. Sixty-five proteins were statistically different between clusters indicating alteration of proteins involved in fatty acid oxidation, muscle structure and regulation. Our results indicate that this method is significantly faster than prior single fiber methods in both data collection and sample preparation while maintaining sufficient proteome depth. We anticipate this assay will enable future studies of single muscle fibers across hundreds of individuals, which has not been possible previously due to limitations in throughput.

biochemistry↗

bioRxiv2023

High Throughput Proteomic Analysis of Heterogeneous Cell Populations with NanoDTSC

Identification and proteomic characterization of rare cell types within complex organ derived cell mixtures is best accomplished by label-free quantitative mass spectrometry. High throughput is required to rapidly survey hundreds to thousands of individual cells to adequately represent rare populations. Here we present parallelized nanoflow dual-trap single-column liquid chromatography (nanoDTSC) operating at 15 minutes of total run time per cell with peptides quantified over 11.5 minutes using standard commercial components, thus offering an accessible and efficient LC solution to analyze 96 single-cells per day. At this throughput, nanoDTSC quantified over 1,000 proteins in individual cardiomyocytes and heterogenous populations of single cells from aorta. For Table of Contents Only O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/522908v4_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@352d49org.highwire.dtl.DTLVardef@1304d2corg.highwire.dtl.DTLVardef@1a9df5org.highwire.dtl.DTLVardef@1785464_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2021

COT: an efficient Python tool for detecting marker genes among many subtypes

We develop an accurate and efficient method to detect marker genes among many subtypes using subtype-enriched expression profiles. We implement a Cosine based One-sample Test (COT) Python software that is easy to use and applicable to multi-omics data. We demonstrate the performance and utility of COT on gene expression and proteomics data acquired from tissue or cell subtypes. Formulated as a one-sample test with Cosine similarity test statistic in scatter space, the detected de novo marker genes will allow biologists to perform a more comprehensive and unbiased molecular characterization, deconvolution and classification of complex tissue or cell subtypes.

bioinformatics↗

bioRxiv2020

A Simple Optimization Workflow to Enable Precise and Accurate Imputation of Missing Values in Proteomic Datasets

Missing values in proteomic data sets have real consequences on downstream data analysis and reproducibility. Although several imputation methods exist to handle missing values, there is no single imputation method that is best suited for a diverse range of data sets and no clear strategy exists for evaluating imputation methods for large-scale DIA-MS data sets, especially at different levels of protein quantification. To navigate through the different imputation strategies available in the literature, we have established a workflow to assess imputation methods on large-scale label-free DIA-MS data sets. We used two distinct DIA-MS data sets with real missing values to evaluate eight different imputation methods with multiple parameters at different levels of protein quantification; dilution series data set and an independent data set with actual experimental samples. We found that imputation methods based on local structures within the data, like local least squares (LLS) and random forest (RF), worked well in our dilution series data set whereas, imputation methods based on global structures within the data, like BPCA performed well in our independent data set. We also found that imputation at the most basic level of protein quantification - fragment level-improved accuracy and number of proteins quantified. Overall, this study indicates that the most suitable imputation method depends on the overall structure and correlations of proteins within the data set and can be identified with the workflow presented here.

bioinformatics↗

bioRxiv2020

A Simultaneous Methylome and Proteome Quantitative Workflow for Complex Samples

Proteoforms containing post-translational modifications (PTMs) represent a degree of functional diversity only harnessed through analytically precise simultaneous quantification of multiple PTMs. Here we present a method to accurately differentiate an unmodified peptide from its PTM-containing counterpart through data-independent acquisition-mass spectrometry, leveraging small precursor mass windows to physically separate modified peptidoforms from each other during MS2 acquisition. We utilize a lysine and arginine PTM-enriched peptide assay library and site localization algorithm to simultaneously localize and quantify seven PTMs including mono-, di-, and tri-methylation, acetylation, and succinylation in addition to total protein quantification in a single MS run without the need to enrich experimental samples. To evaluate biological relevance, this method was applied to liver lysate from differentially methylated non-alcoholic steatohepatitis (NASH) mouse models. We report altered methylation and acetylation together with total protein changes drive the novel hypothesis of a regulatory function of PTMs in protein synthesis and mRNA stability in NASH. Graphical Abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

systems biology↗

bioRxiv2019

MitoPlex: A Targeted Multiple Reaction Monitoring Assay for Quantification of a Curated Set of Mitochondrial Proteins

Mitochondria are the major source of cellular energy (ATP), as well as critical mediators of widespread functions such as cellular redox balance, apoptosis, and metabolic flux. Methods to quantify mitochondrial content are limited to low throughput immunoassays, measurement of mitochondrial DNA, or relative quantification by untargeted mass spectrometry. Here, we present a high throughput, reproducible and quantitative mass spectrometry multiple reaction monitoring based assay of 37 proteins critical to central carbon chain metabolism and overall mitochondrial function termed MitoPlex. We coupled this protein multiplex with a parallel analysis of the central carbon chain metabolites (218 metabolite assay) extracted in tandem from the same sample, be it cells or tissue. In tests of its biological applicability in cells and tissues, MitoPlex plus metabolites indicated profound effects of HMG-CoA Reductase inhibition (e.g., statin treatment) on mitochondria of i) differentiating C2C12 skeletal myoblasts, as well as a clear opposite trend of statins to promote mitochondrial protein expression and metabolism in heart and liver, while suppressing mitochondrial protein and ii) aspects of metabolism in the skeletal muscle obtained from C57Bl6 mice. Our results not only reveal new insights into the metabolic effect of statins in skeletal muscle, but present a new high throughput, reliable MS-based tool to study mitochondrial dynamics in both cell culture and in vivo models.

molecular biology↗

bioRxiv2018

Multi-omic profiling of TKI resistant K562 cells suggests metabolic reprogramming to promote cell survival

Resistance to chemotherapy can occur through a wide variety of mechanisms. Resistance to tyrosine kinase inhibitors (TKIs) often arises from kinase mutations-however, \"off-target\" resistance occurs but is poorly understood. Previously, we established cell line resistance models for three TKIs used in chronic myeloid leukemia treatment, and found that resistance was not attributed entirely to failure of kinase inhibition. Here, we performed global, integrated proteomic and transcriptomic profiling of these cell lines to describe mechanisms of resistance at the protein and gene expression level. We used whole transcriptome sequencing and SWATH-based data-independent acquisition mass spectrometry (DIA-MS), which does not require isotopic labels and provides quantitative measurements of proteins in a comprehensive, unbiased fashion. The proteomic and transcriptional data were correlated to generate an integrated understanding of the gene expression and protein alterations associated with TKI resistance. We defined mechanisms of resistance and two novel markers, CA1 and alpha-synuclein, that were common to all TKIs tested. Resistance to all of the TKIs was associated with oxidative stress responses, hypoxia signatures, and apparent metabolic reprogramming of the cells. Metabolite profiling and glucose-dependence experiments showed that resistant cells had routed their metabolism through glycolysis (particularly through the pentose phosphate pathway) and exhibited disruptions in mitochondrial metabolism. These experiments are the first to report a global, integrated proteomic, transcriptomic and metabolic analysis of TKI resistance. These data suggest that although the mechanisms are complex, targeting metabolic pathways along with TKI treatment may overcome pan-TKI resistance.\n\nKey PointsO_LIAlterations to metabolism are a common feature of target-mutation-independent resistance in CML cells across multiple clinically relevant TKIs.\nC_LIO_LICarbonic anhydrase 1 (CA1) and a-synuclein (SNCA) are novel markers of metabolic reprogramming in TKI resistant CML cells.\nC_LI

cancer biology↗