Fusion genes are known to be key drivers of tumor growth in several types of cancer. Traditionally, detecting fusion genes has been a difficult task based on fluorescent in situ hybridization to ...detect chromosomal abnormalities. More recently, RNA sequencing has enabled an increased pace of fusion gene identification. However, RNA-Seq is inefficient for the identification of fusion genes due to the high number of sequencing reads needed to detect the small number of fusion transcripts present in cells of interest. Here we describe a method, Single Primer Enrichment Technology (SPET), for targeted RNA sequencing that is customizable to any target genes, is simple to use, and efficiently detects gene fusions. Using SPET to target 5701 exons of 401 known cancer fusion genes for sequencing, we were able to identify known and previously unreported gene fusions from both fresh-frozen and formalin-fixed paraffin-embedded (FFPE) tissue RNA in both normal tissue and cancer cells.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Biologists have long desired to understand multi-cellular processes at the resolution of the single cell. Tremendous efforts have been made over more than a century to decipher biology at the single ...cell level from the advent of immunohistochemistry to high-plex multi-parametric cytometry. More recently, technological developments in extracting and labelling nucleic acids from single cells have boosted single-cell information acquisition to include the genome, transcriptome, epigenome, proteome, and more, even simultaneously collecting data from multiple modalities. Here we will review some of the original motivations that have driven the development of new single cell tools, providing perspective on why these new tools were created and which tools we hope to see developed in the future.
Traditional methods for selecting aptamers require multiple rounds of selection and optimization in order to identify aptamers that bind with high affinity to their targets. Here we describe an assay ...that requires only one round of positive selection followed by high-throughput DNA sequencing and informatic analysis in order to select high-affinity aptamers. The assay is flexible, requires less hands on time, and can be used by laboratories with minimal expertise in aptamer biology to quickly select high-affinity aptamers to a target of interest. This assay has been utilized to successfully identify aptamers that bind to thrombin with dissociation constants in the nanomolar range.
Olfactory neurons project their axons to spatially invariant glomeruli in the olfactory bulb, forming an ordered pattern of innervation comprising the olfactory sensory map. A mirror symmetry exists ...within this map, such that neurons expressing a given receptor typically project to one glomerulus on the medial face and one glomerulus on the lateral face of the bulb. The mechanisms underlying an olfactory neuron's choice to project medially versus laterally remain largely unknown, however. Here we demonstrate that insulin-like growth factor (IGF) signaling is required for sensory innervation of the lateral olfactory bulb. Mutations that eliminate IGF signaling cause axons destined for targets in the lateral bulb to shift to ectopic sites on the ventral-medial surface. Using primary cultures of olfactory and cerebellar neurons, we further show that IGF is a chemoattractant for axon growth cones. Together these observations reveal a role of IGF signaling in sensory map formation and axon guidance.
Secreted proteins play critical roles in cellular communication. Methods enabling concurrent measurement of cellular protein secretion, phenotypes and transcriptomes are still unavailable. Here we ...describe time-resolved assessment of protein secretion from single cells by sequencing (TRAPS-seq). Released proteins are trapped onto the cell surface and probed by oligonucleotide-barcoded antibodies before being simultaneously sequenced with transcriptomes in single cells. We demonstrate that TRAPS-seq helps unravel the phenotypic and transcriptional determinants of the secretion of pleiotropic T
1 cytokines (IFNγ, IL-2 and TNF) in activated T cells. In addition, we show that TRAPS-seq can be used to track the secretion of multiple cytokines over time, uncovering unique molecular signatures that govern the dynamics of single-cell cytokine secretions. Our results revealed that early central memory T cells with CD45RA expression (T
) are important in both the production and maintenance of polyfunctional cytokines. TRAPS-seq presents a unique tool for seamless integration of secretomics measurements with multi-omics profiling in single cells.
Background: Multiple Myeloma (MM) is the second most common hematologic malignancy and remains incurable. Daratumumab (dara) is a potent anti-CD38 monoclonal antibody used for MM treatment, but ...responses are heterogeneous, and resistance is inevitable. There is hence an urgent need for biomarkers to predict the response to dara. We hypothesized that gene expression profiles (GEP) of tumor subpopulations could be used to predict the response to dara-based therapy. Methodology: We used the Enhanced Single-Cell Analysis with Protein Expression (ESCAPE) RNA-Seq platform (Singleron Biotechnologies) to simultaneously profile the gene and protein expression of plasma cells from the bone marrow of 15 MM patients prior to treatment with dara-based regimens. These data were combined with publicly available single-cell data from Cohen et al. 2021, resulting in a total of 32 samples. Response was defined based on the international myeloma working group (IMWG) criteria, considering patients achieving a very good partial response (VGPR) or better as “responders,” while those achieving a partial response (PR) or worse were deemed “non-responders”. The data were split into 23 training samples (15 responders and 8 non-responders) and 9 validation samples (3 responders and 6 non-responders), each set containing a combination of cases from the two sources of single-cell data. An interpretable machine-learning model was then built to predict the response of each individual cell. The median response score for each sample was used to predict the patient-level response. The Shapley Additive Explanations (SHAP) values from the dara response prediction model were then used as a starting point for creating a general prognostic model. The 200 most influential genes from the dara response prediction model were used to create a linear response model based on the TCGA (MMRF-Compass) bulk RNAseq dataset. The bulk RNAseq dataset was split 80:20 into training and test datasets. Results from the test dataset are presented here. Results: We analyzed single-cell data from a total of 13,094 plasma cells from 32 BM samples. These included 17 cases of relapsed MM described by Cohen et al., who were treated with dara, carfilzomib, lenalidomide, and dexamethasone. The patients treated at our centre comprised 7 cases of relapsed MM treated with dara, thalidomide, and dexamethasone, and 8 samples from patients with newly diagnosed MM treated with dara, bortezomib, and dexamethasone on clinical trials. All 9 patients in the test dataset had their responses correctly predicted (Fig 1). A separate cohort of non-dara-treated patient samples was also tested using the same model, and the response prediction was not accurate (AUC=0.67), suggesting that this model has specificity for predicting response to dara and is not a general response classifier (data not shown). Based on the SHAP values from the dara response prediction model, we developed a prognostic model from bulk RNAseq data consisting of four genes. A combination of this four-gene signature plus the international staging system (ISS) score resulted in a superior prediction of overall survival compared to the ISS alone with a hazard ratio of 13.5 compared to 2.5 for the ISS alone in the same dataset (Fig 2). Conclusions: Combining single-cell RNA sequencing with machine-learning methods may have value in predicting response to immunotherapy in MM. We propose that gene signatures derived from single-cell data may augment clinical decision-making for determining treatment allocation. We also show that these signatures may be combined with existing prognostic scores, resulting in improved delineation of risk groups. Furthermore, the applicability of our gene signature to a bulk gene expression dataset enhances its clinical relevance. Our study demonstrates the power of single-cell omics to identify novel predictors of response to therapy and prognosis in MM, which may be translated into clinical use.
Background
Alzheimer’s disease (AD) patients show sustained levels of inflammation in the brain and the peripheral immune system. It is not known how various peripheral blood mononuclear cells ...(PBMCs) differ in AD patients and whether those differences can act as biomarkers of AD. Here we performed a multi‐omic profiling of PBMCs from AD patients and compared the composition of their cell type and cell state as well as their gene and protein expression to normal controls.
Method
Single‐cell proteogenomics analysis was performed on PBMCs from 20 AD patients and 15 controls using Singleron Biotechnologies’ ESCAPE platform. Seven of the AD and four control samples were additionally analyzed for bulk protein expression using Sciomics’ scioDiscover platform.
Result
Bulk proteomics identified 100 proteins with a significant differential abundance between AD patients and controls. Data point to a higher platelet activation and degranulation, as well as changes in the EGFR / MAPK3 and VEGF signaling in AD. As an individual marker CD163 was identified at a higher abundance in AD PBMCs pointing to an increase in monocyte / macrophage activity.
From the single‐cell analysis we found that AD patients had significantly more CD14+ monocytes. We further found that the CD14+ monocytes could be split into seven clusters based on their gene expression with only two clusters having a significantly higher number of cells in AD patients. One of the overrepresented clusters showed high expression of Alarmin genes, suggesting an increased inflammatory environment, while the other cluster showed a higher level of HLA expression suggesting a state primed for activation.
Conclusion
We found significant changes in both gene and protein expression in PBCMs from AD patients that indicate an increased inflammatory state . While there was a good overlap of findings between gene and protein expression, some of the changes are only seen at the protein level, while others are only observed at the level of gene expression. The combination of the two measurement techniques provides us with additional insights into inflammatory nature of the peripheral immune system in AD patients and provide hints at the mechanisms different cells use to generate those inflammatory signals.
Elucidating the molecular changes that arise during neural differentiation and fate specification is crucial for building accurate in vitro models of neurodegenerative diseases using human embryonic ...stem cells (hESCs). Here we review the importance of hESCs and derived progenitors in treating and modeling neurological diseases, and summarize the current efforts for the differentiation of hESCs into neural progenitors and defined neurons. We recapitulate the recent findings and discuss open questions on aspects of molecular control of gene expression by chromatin modification and methylation, posttranscriptional regulation by alternative splicing and the action of microRNAs, and protein modification. An integrative view of the different levels will hopefully provide much needed insight into understanding stem cell biology.
Abstract only
e20013
Background: Multiple Myeloma (MM) is an incurable plasma cell (PC) malignancy and high risk (HR) MM remains an unmet clinical need. Translocation 4;14 occurs in 15% of MM and is ...associated with an adverse prognosis. A deeper understanding of the biology and immune micro-environment of t(4;14) MM is necessary for the development of effective targeted therapies. Single Cell multi-omics provides a new tool for phenotypic characterization of MM. Here we used Proteona’s ESCAPE™ single cell multi-omics platform to study a cohort of patients with t(4;14) MM. Methods: Diagnostic bone marrow (BM) samples from 14 patients with t(4;14) MM were analysed using the ESCAPE platform from Proteona which simultaneously measures gene and cell surface protein expression in single cells. Cryopreserved BM samples were stained with 65 DNA barcoded antibodies and subsequently sorted on CD138 expression. The CD138 positive and negative fractions were recombined at a known ratio for analysis using the 10x Genomics 3’ RNAseq kit. Resulting data were analyzed with Seurat and MapCell. Results: The patients had a median age of 63 years. All received novel agent based induction. Median progression free and overall survival (PFS and OS) were 22 and 34 months respectively. MMSET was overexpressed in all PCs while FGFR3 expression could be categorized into zero cells expressing FGFR3, low expression (< 10% of cells expressing FGFR3) or high expression (> 80% of cells expressing FGFR3). We also found heterogeneity in the expression of cancer testis antigens (CTA) such as FA133A and CTAG2 between PC clusters across samples. Variation in the immune microenvironment of the BM was seen across all patient samples with no correlation between cell types and PFS or OS. However, an analysis of BM samples at diagnosis and relapse in one patient showed a shift in the ratio of T cells to CD14 monocytes with a ratio of 5.7 at diagnosis compared to 0.6 at relapse. Further analysis of PCs in this patient found 8 PC populations, each containing variable numbers of cells from both the diagnostic and relapse samples. This suggests that all populations present at relapse were also present at diagnosis, although at variable proportions. Increased expression of RCAN3 (associated with cereblon depletion) was detected at relapse. Conclusions: We present the first application of single cell multi-omics immune profiling in high risk MM. The heterogeneity in expression of CTA has implications for the application of immunotherapies, while the upregulation of RCAN3 may explain failure of immunomodulatory therapy. Our small sample size may explain the lack of correlation between gene or protein expression with clinical outcomes. We propose that t(4;14) MM is a genomically and immunologically heterogeneous disease. Single cell analysis of larger cohorts is required to build on our findings.
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