A platform-agnostic approach to single-cell genomic applications
Single cell genomics is a powerful approach to uncover cellular heterogeneity in normal and disease tissues, and understanding molecular mechanisms of development and disease. However, preparing samples for processing and isolating or capturing a sufficient number of single cells to be able to answer the research question of interest is challenging. The wide ranges of tissue architecture, and cellular sizes and shapes calls for the availability of platforms that can efficiently recover sufficient numbers of single cells for the assays of interest. In addition to the diversity in cell sizes and shapes, there is a wide range of starting numbers of cells and viability that is available for processing. In order to ensure successful generation of single cell libraries from a wide range of starting material, at MedGenome we have integrated several of the well-validated single cell isolation and library preparation platforms.
TCR sequencing solutions at MedGenome
In this whitepaper, we present MedGenome’s NGS based workflows for profiling of the TCR repertoire: namely a) Bulk TCR profiling using: SMARTer TCR α /β Profiling Kit (Takara Bio USA Inc) and modifications to the protocol for Gamma/Delta and FFPE TCR repertoire profiling, b) Single cell TCR Profiling using: 10X Genomics Chromium Immune Profiling solutions, and Takara single-cell TCR sequencing kits. We also present an overview of the types of samples we have processed in-house and application.
T Cell Receptor (TCR) Repertoire Sequencing
In this technical sheet, we present information on the workflows for TCR sequencing at MedGenome and also present the sample types that we can process. We also present application data using SMARTer TCR α/β Profiling Kit and 10x Genomics chromium platform downstream of neoantigen vaccine screening platform developed at MedGenome : OncoPeptVAC.
T Cell Receptor (TCR) repertoire sequencing from FFPE samples
In this white-paper we present data generated by using a modified protocol of the SMARTer® TCR Proﬁling Kit to perform TCR sequencing and analysis of tumor-infiltrating lymphocytes (TILs) from a FFPE tumor tissue block.
We accept sample both as RNA (100 ng minimum) or 5 μm unstained FFPE tumor tissue blocks.
OncoPeptTUMETM — A novel in-silico approach to model the tumor microenvironment and predict treatment efficacy and long-term survival benefits for immunotherapy applications
Cancer immunotherapy is now established as a major therapeutic modality, and 70% of all cancer patients are estimated to receive some form of immunotherapy treatment as a part of their disease control by 2025. Cancer immunotherapy drugs elicit their anti-tumor immune response in a subset of the treated patients by activating CD8 T-cells and provide sustainable and long-lasting benefit in a few. Recently significant efforts have been devoted to understanding the factors that influence response to immuno-therapy or contribute to the development of resistance to therapy. While it is appreciated that many different tumor cell- intrinsic and extrinsic features, including the tumor microenvironment, driver gene mutations, host genetics, microbiome and environmental factors modulate response to immune checkpoint inhibitors , the tumor microenvironment ecosystem could be a major contributor in regulating response to immunotherapy and development of resistance [2,3].
In this whitepaper, we present key features and highlight some case studies using the Diabetome Knowledgebase. The Diabetome contains multiple data points collected over 25 years on over 300,000 Type 1 and Type 2 diabetes patients. Information available in the database includes well-characterized clinical phenotypes, biochemical investigations, pharmaceutical prescriptions, genotype mapping, complications of diabetes, pedigree charts & basic statistical tools. By utilizing this integrated solution, researchers can stratify patients into sub-groups based on parameters such as rapid deteriorates, and differential therapeutic responders (positive and negative). This will allow researchers to further study the underlying mechanisms of the identified phenotypes by correlating with their clinical data, predict risk of diabetes, and find molecular features unique to the subgroups and help in identifying ideal treatment modalities for the sub-groups. Taken together, the Diabetome is a powerful tool to facilitate accurate drug target prediction and novel discoveries. In this white paper, we highlight the a) key features of the data-sets present , b) provide an overview of the output of the filters c) highlight examples of the types of data available for complications of the disease and show data on analysis and stratification of patients based on therapy.
Ophthatome Knowledgebase: Over 500,000 Clinical Phenotype Records for Ocular Research
To enable genomic, pharmacogenomic and clinical research and discovery for ocular diseases, MedGenome has launched the OphthatomeTM Knowledgebase. This knowledgebase of ocular diseases is a comprehensive collection of clinical, phenotype and biochemical data providing researchers and clinicians with a platform to design studies that address critical unmet needs in eye disorders. The searchable interface allows end users to build complex queries to select disease cohorts based on organs affected, disease type and subtype, the age of disease onset, drug response and many other clinical and phenotypic parameters.