Assessment of reproducibility in multiplex immunohistochemistry
Multiplex Immunohistochemistry (mlHC) is an advanced technique that can detect tissue-based biomarkers simultaneously, which transforms the traditional approach of immunohistochemistry. mlHC enables precision medicine in both research and clinical practice because it evaluates various proteins and their spatial distribution within single tissue sections at a cellular level.
Specific requirements are crucial for achieving high-quality staining and analysis, and to deem a mlHC assay as validated, it must be proven to be analytically reproducible.
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An automated complementary method for spatially resolved quantitative analysis of drugs and biomarkers
Having an initial understanding of drug distribution, quantification and target engagement within disease-relevant histological structures is critical in choosing more effective drug candidates. MALDI MSI unveils the quantitative distribution of label-free drugs, or biomarkers, providing valuable data that complements information obtained through traditional approaches.
Our optimized workflows offer solutions to problems that are sometimes encountered when you quantify drugs and biomarkers with MSI, and open the door for precision quantification of any molecule in specific regions of interest.
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Batch alignment of mass spectrometry imaging (MSI) metabolome through data integration
Mass spectrometry imaging (MSI) is a critical tool used to investigate tissues and molecules in spatial bioanalysis. Technical challenges in MSI, also known as batch-effects, have proven to impede reliable comparison of data from large-scale studies performed in translational clinical research.
Meaningful analysis of data generated in large-scale studies is critical for medicine and biology studies. This application note focuses on a batch correction method designed to minimize impedance of batch-effects and allow reliable identification of biological clusters and their comparison.
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Automated approach to quantify single molecule in complex tissues
This application note focuses on an efficient analysis method which enables the quantification of molecules at the single-cell level. Accurate RNA quantification in single molecules is crucial to understand the dynamics of gene expression and regulation.
In this study, individual molecules were imaged in fixed cells and DNA staining was performed in tumoral areas of the tissue. Analysis results showcase quantification of single cells and provide an understanding of cell-to-cell interactions in cancer cells and other molecules.
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RNAscope assay: Single cell transcriptomics for drug target discovery
When dealing with biomarkers in drug target discovery, profiling the tissue transcriptome in its spatial organization is key to understanding and predicting that tissue’s response to immunotherapy, because the tissue function within the body relies on the precise spatial organization of the cells. This is especially true when dealing with complex and heterogeneous tissues, such as tumors.
With tumors, the relationship between the cells and their environment is what ultimately shapes a patient’s fate. Applying the RNAscopeTM assay allows that evaluation of the presence of transcripts within a spatial context. This technique then ultimately can predict the tumor’s response to immunotherapy.
To further explain the impact of the RNAscope assay, this application note details a case study on a non-small-cell lung cancer (NSCLC) sample to predict its response to immunotherapy. This case study:
- Investigates the presence of the PD-L1 drug target transcript and the Granzyme B (GZMB) biomarker of clinical outcome transcript
- Explains quality control measures taken to validate the hybridization technique
Looks at the heterogenous distribution of transcripts in the NSCLC tissue section & examines the quantification of the PD-L` and GZMB to draw conclusions on the tissue’s reaction
Personalized therapy selection: Investigation of multiple immune-checkpoints
PD1 blockade has changed the immunotherapy approach against solid tumors – prescribing patients’ monoclonal antibody-based therapy yielded positive results. However, the number of patients that benefited was small compared to expectations, and lack of attention paid to immunohistochemistry could be responsible. Understanding the tumor microenvironment with a single biomarker is not accurate enough to predict the interaction of the drug with the site of action, or to predict the effectiveness of the drug for the patient.
In this application note, industry expert, Corinne Ramos, PhD, discusses how understanding the tumor microenvironment (TME) at the biomarker level is not enough to predict the interaction of the drug with the site of action. Her study performs a deep spatial profiling of biomarkers on two baseline non-small cell lung cancer (NSCLC) tissue samples. The population of gene expression was characterized with a focus on a specific region of interest on the tissue where she could draw conclusions on the TME.
Her research walks through:
- Immune infiltration
- How phenotype correlates with ICI response
- T-cell-inflamed gene-expression profiles
- Immune suppressive pathways that reduce ICI activity
- Tight immune checkpoint targeting.
Learn why the TME needs to be investigated at the molecular level to accurately prescribe the appropriate therapy.