This chapter explores methods for antibody conjugation and validation, staining procedures, and preliminary data acquisition with IMC or MIBI in human and mouse pancreatic adenocarcinoma specimens. The protocols' goal is to enable the application of these intricate platforms, not limited to tissue-based tumor immunology investigations, but also extending to wider tissue-based oncology and immunology studies.
Complex signaling and transcriptional programs are integral to the development and physiology of specialized cell types. The origins of human cancers, stemming from a variety of specialized cell types and developmental stages, are linked to genetic disruptions in these regulatory programs. Developing effective immunotherapies and identifying viable drug targets hinges on a thorough understanding of these multifaceted biological systems and their potential to initiate cancer. The pioneering integration of single-cell multi-omics technologies, which analyze transcriptional states, has been accompanied by the expression of cell-surface receptors. This chapter explains a computational framework, SPaRTAN (Single-cell Proteomic and RNA-based Transcription factor Activity Network), that establishes a connection between transcription factors and the expression of proteins on the cell's surface. Using CITE-seq (cellular indexing of transcriptomes and epitopes by sequencing) data and cis-regulatory sites, SPaRTAN builds a model depicting how transcription factors and cell-surface receptors' interactions influence gene expression. Our presentation of the SPaRTAN pipeline uses CITE-seq data from peripheral blood mononuclear cells.
Mass spectrometry (MS) is a crucial analytical tool in biological research, with the ability to investigate a variety of biomolecules—proteins, drugs, and metabolites—something that alternative genomic platforms often fall short of achieving. Trying to assess and incorporate measurements from multiple molecular classes makes downstream data analysis complicated, requiring input from experts across different relevant fields. The intricate nature of this process acts as a critical impediment to the widespread implementation of MS-based multi-omic methodologies, despite the unparalleled biological and functional understanding that these data offer. Genetics research Our group designed Omics Notebook, an open-source framework to automatically, reproducibly, and customizably facilitate the exploration, reporting, and integration of mass spectrometry-based multi-omic data to meet this unmet need. This pipeline's deployment provides researchers with a framework to more quickly identify functional patterns across complex data types, concentrating on results that are both statistically significant and biologically compelling in their multi-omic profiling. The current chapter details a protocol, utilizing our publicly accessible tools, that analyzes and integrates high-throughput proteomics and metabolomics data for the creation of reports designed to bolster impactful research, cross-institutional partnerships, and broader data distribution.
Protein-protein interactions (PPI) are integral to a range of biological processes, including the mechanisms of intracellular signal transduction, gene transcription, and metabolic activity. PPI's participation in the pathogenesis and development of various diseases, cancer being a prime example, is acknowledged. Molecular detection technologies, coupled with gene transfection, have provided insights into the PPI phenomenon and its functions. In contrast, histopathological investigation, even though immunohistochemical analyses illuminate the expression and localization of proteins within pathologic tissues, has struggled to display protein-protein interactions. An in situ proximity ligation assay (PLA) was devised to microscopically depict protein-protein interactions (PPI) within the context of formalin-fixed, paraffin-embedded tissues, cultivated cells, and frozen tissues. Employing PLA on histopathological specimens enables thorough cohort studies of PPI, thus shedding light on PPI's impact on pathology. Our prior studies highlighted the dimerization pattern of estrogen receptors and the implications of HER2-binding proteins, using fixed formalin-preserved embedded breast cancer tissue. We detail in this chapter a technique for visualizing protein-protein interactions (PPIs) using photolithographic arrays (PLAs) in pathological specimens.
Nucleoside analogs (NAs), a broadly recognized class of anticancer agents, are clinically administered for diverse cancer treatments, sometimes as a single therapy or in conjunction with other well-established anticancer or pharmacological agents. So far, nearly a dozen anticancer nucleic acid drugs have been approved by the FDA, and various novel nucleic acid agents are undergoing preliminary and clinical trials for potential future applications. https://www.selleckchem.com/products/dir-cy7-dic18.html Unfortunately, tumor cell resistance to therapy often stems from the inadequate delivery of NAs, which is directly linked to changes in the expression of drug carrier proteins (like solute carrier (SLC) transporters) found in the tumor cells or the cells surrounding the tumor microenvironment. In hundreds of patient tumor tissues, researchers can simultaneously analyze alterations in numerous chemosensitivity determinants using the superior high-throughput approach of tissue microarray (TMA) combined with multiplexed immunohistochemistry (IHC), thereby surpassing conventional IHC. This chapter presents a detailed procedure, optimized in our laboratory, for multiplexed IHC, including image acquisition and marker quantification on tissue microarrays from pancreatic cancer patients treated with gemcitabine. We illustrate the steps, analyze resulting data, and discuss essential considerations for the design and performance of such experiments.
Cancer therapy often encounters the challenge of innate or treatment-induced resistance to anticancer medications. Recognizing the patterns of drug resistance can be key in developing new and distinct therapeutic solutions. One method involves applying single-cell RNA sequencing (scRNA-seq) to both drug-sensitive and drug-resistant variant samples, followed by network analysis of the scRNA-seq data to reveal pathways related to drug resistance. This computational analysis pipeline, outlined in this protocol, investigates drug resistance by applying the Passing Attributes between Networks for Data Assimilation (PANDA) tool to scRNA-seq expression data. PANDA, an integrative network analysis tool, incorporates protein-protein interactions (PPI) and transcription factor (TF) binding motifs.
The field of biomedical research has been revolutionized by the rapid emergence of spatial multi-omics technologies, a recent phenomenon. Spatial transcriptomics and proteomics have found significant assistance in the Digital Spatial Profiler (DSP), a product of nanoString, for tackling complex biological questions. Our three years of hands-on experience in the DSP domain have led to the development of a comprehensive, detailed protocol and key management guide that can assist the broader community in streamlining their processes.
Within the 3D-autologous culture method (3D-ACM), a patient's own body fluid or serum is integral in constructing both a 3D scaffold and the culture medium for patient-derived cancer samples. Microarray Equipment Within a 3D-ACM model, tumor cells and/or tissues extracted from a patient can multiply in a laboratory setting, perfectly reproducing the characteristics of their in vivo environment. The aim is to preserve, to the greatest extent possible, the native biological properties of the tumor in a cultural environment. This technique has been utilized in two model types: (1) cells extracted from malignant ascites or pleural effusions (body fluids), and (2) solid tissues obtained from biopsies or surgically excised cancers. The 3D-ACM models' detailed procedures are described in the following sections.
Understanding disease pathogenesis is advanced by the unique capabilities of the mitochondrial-nuclear exchange mouse model, specifically in the area of mitochondrial genetics. We detail the reasoning behind their creation, the procedures employed in their development, and a concise overview of how MNX mice have been used to investigate the roles of mitochondrial DNA in various diseases, particularly cancer metastasis. Mitochondrial DNA variations, unique to different mouse lineages, exhibit both intrinsic and extrinsic impacts on metastatic efficiency by altering epigenetic patterns in the nuclear genome, impacting reactive oxygen species production, modulating the gut microbiota, and affecting the immune response against cancer cells. This report, being dedicated to the issue of cancer metastasis, nonetheless acknowledges the significant contribution of MNX mice to the understanding of mitochondrial roles in various other diseases.
The high-throughput technique, RNA sequencing (RNA-seq), is utilized for the quantification of mRNA within a biological sample. Differential gene expression analysis between drug-resistant and sensitive cancer types is frequently employed to pinpoint genetic factors that contribute to drug resistance. We describe a complete methodology, incorporating experimental steps and bioinformatics, for the isolation of mRNA from human cell lines, the preparation of mRNA libraries for next-generation sequencing, and the subsequent bioinformatics analysis of the sequencing data.
The occurrence of DNA palindromes, a type of chromosomal alteration, is a frequent hallmark of tumorigenesis. Identical nucleotide sequences to their reverse complements typify these entities. These sequences frequently stem from inappropriate DNA double-strand break repair, telomere fusions, or stalled replication forks, all of which represent typical adverse early events associated with cancer development. This document details a protocol for enriching palindromes from low-input genomic DNA sources and describes a bioinformatics tool for evaluating the enrichment efficiency and determining the precise genomic locations of de novo palindrome formation from low-coverage whole-genome sequencing.
The holistic understanding of cancer biology is advanced by the rigorous methodologies of systems and integrative biology. A deeper mechanistic understanding of the control, execution, and functioning of intricate biological systems stems from integrating lower-dimensional data and results from lower-throughput wet laboratory studies into in silico discoveries utilizing large-scale, high-dimensional omics data.