scholarly journals Models that combine transcriptomic with spatial protein information exceed the predictive value for either single modality

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Ioannis A. Vathiotis ◽  
Zhi Yang ◽  
Jason Reeves ◽  
Maria Toki ◽  
Thazin Nwe Aung ◽  
...  
Keyword(s):  
Clinical Outcomes ◽  
Diagnostic Test ◽  
Spatial Information ◽  
Cancer Therapeutics ◽  
Quantitative Information ◽  
Spatially Resolved ◽  
Companion Diagnostic ◽  
Mrna Gene ◽  
Rna Expression Profiling ◽  
Tumor Types

Immunotherapy has reshaped the field of cancer therapeutics but the population that benefits are small in many tumor types, warranting a companion diagnostic test. While immunohistochemistry (IHC) for programmed death-ligand 1 (PD-L1) or mismatch repair (MMR) and polymerase chain reaction (PCR) for microsatellite instability (MSI) are the only approved companion diagnostics others are under consideration. An optimal companion diagnostic test might combine the spatial information of IHC with the quantitative information from RNA expression profiling. Here, we show proof of concept for combination of spatially resolved protein information acquired by the NanoString GeoMx® Digital Spatial Profiler (DSP) with transcriptomic information from bulk mRNA gene expression acquired using NanoString nCounter® PanCancer IO 360™ panel on the same cohort of immunotherapy treated melanoma patients to create predictive models associated with clinical outcomes. We show that the combination of mRNA and spatially defined protein information can predict clinical outcomes more accurately (AUC 0.97) than either of these factors alone.

scholarly journals Ultrafast non-excitonic valley Hall effect in MoS2/WTe2 heterobilayers

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jekwan Lee ◽  
Wonhyeok Heo ◽  
Myungjun Cha ◽  
Kenji Watanabe ◽  
Takashi Taniguchi ◽  
...  
Keyword(s):  
Hall Effect ◽  
Quantitative Information ◽  
Exciton Energy ◽  
Electron Hole ◽  
Kerr Rotation ◽  
Polarized Electrons ◽  
Spatially Resolved ◽  
Long Valley ◽  
Bound Electron ◽  
Valley Hall Effect

AbstractThe valley Hall effect (VHE) in two-dimensional (2D) van der Waals (vdW) crystals is a promising approach to study the valley pseudospin. Most experiments so far have used bound electron-hole pairs (excitons) through local photoexcitation. However, the valley depolarization of such excitons is fast, so that several challenges remain to be resolved. We address this issue by exploiting a unipolar VHE using a heterobilayer made of monolayer MoS2/WTe2 to exhibit a long valley-polarized lifetime due to the absence of electron-hole exchange interaction. The unipolar VHE is manifested by reduced photoluminescence at the MoS2 A exciton energy. Furthermore, we provide quantitative information on the time-dependent valley Hall dynamics by performing the spatially-resolved ultrafast Kerr-rotation microscopy; we find that the valley-polarized electrons persist for more than 4 nanoseconds and the valley Hall mobility exceeds 4.49 × 103 cm2/Vs, which is orders of magnitude larger than previous reports.

scholarly journals Spatially resolved transcriptomics in immersive environments

2022 ◽  
Vol 5 (1) ◽  
Author(s):  
Denis Bienroth ◽  
Hieu T. Nim ◽  
Dimitar Garkov ◽  
Karsten Klein ◽  
Sabrina Jaeger-Honz ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Spatial Information ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Immersive Environments ◽  
Visualization System ◽  
Head Mounted Display ◽  
Spatially Resolved ◽  
Expression Of Genes ◽  
Fish Tank

AbstractSpatially resolved transcriptomics is an emerging class of high-throughput technologies that enable biologists to systematically investigate the expression of genes along with spatial information. Upon data acquisition, one major hurdle is the subsequent interpretation and visualization of the datasets acquired. To address this challenge, VR-Cardiomicsis presented, which is a novel data visualization system with interactive functionalities designed to help biologists interpret spatially resolved transcriptomic datasets. By implementing the system in two separate immersive environments, fish tank virtual reality (FTVR) and head-mounted display virtual reality (HMD-VR), biologists can interact with the data in novel ways not previously possible, such as visually exploring the gene expression patterns of an organ, and comparing genes based on their 3D expression profiles. Further, a biologist-driven use-case is presented, in which immersive environments facilitate biologists to explore and compare the heart expression profiles of different genes.

scholarly journals Explainable multi-view framework for dissecting intercellular signaling from highly multiplexed spatial data

2021 ◽  
Author(s):  
Jovan Tanevski ◽  
Attila Gabor ◽  
Ricardo Ramirez Flores ◽  
Denis Schapiro ◽  
Julio Saez-Rodriguez
Keyword(s):  
Breast Cancer ◽  
Spatial Data ◽  
Spatial Information ◽  
Omics Data ◽  
Intercellular Signaling ◽  
Mass Cytometry ◽  
Learning Framework ◽  
Spatially Resolved ◽  
Scalable Methods ◽  
Improve Patient

Abstract The advancement of technologies to measure highly multiplexed spatial data requires the development of scalable methods that can leverage the spatial information. We present MISTy, a flexible, scalable and explainable machine learning framework for extracting interactions from any spatial omics data. MISTy builds multiple views focusing on different spatial or functional contexts to dissect different effects, such as those from direct neighbours versus those from distant cells. MISTy can be applied to different spatially resolved omics data with dozens to thousands of markers, without the need to perform cell-type annotation. We evaluate the performance of MISTy on an in silico dataset and demonstrate its applicability on three breast cancer datasets, two measured by imaging mass cytometry and one by Visium spatial transcriptomics. We show how we can estimate interactions coming from different spatial contexts that we can relate to tumor progression and clinical features. Our analysis also reveals that the estimated interactions in triple negative breast cancer are associated with clinical outcomes which could improve patient stratification. Finally, we demonstrate the flexibility of MISTy to integrate different kinds of views by modeling activities of pathways estimated from gene expression in a spatial context to analyse intercellular signaling.

scholarly journals Spatially resolved metabolomics to discover tumor-associated metabolic alterations

2018 ◽  
Vol 116 (1) ◽  
pp. 52-57 ◽  
Author(s):  
Chenglong Sun ◽  
Tiegang Li ◽  
Xiaowei Song ◽  
Luojiao Huang ◽  
Qingce Zang ◽  
...  
Keyword(s):  
Metabolic Pathways ◽  
Fatty Acid Biosynthesis ◽  
Spatial Information ◽  
Tumor Therapy ◽  
Metabolic Enzymes ◽  
Metabolic Reprogramming ◽  
Glutamine Metabolism ◽  
Ionization Mass ◽  
Metabolic Alterations ◽  
Spatially Resolved

Characterization of tumor metabolism with spatial information contributes to our understanding of complex cancer metabolic reprogramming, facilitating the discovery of potential metabolic vulnerabilities that might be targeted for tumor therapy. However, given the metabolic variability and flexibility of tumors, it is still challenging to characterize global metabolic alterations in heterogeneous cancer. Here, we propose a spatially resolved metabolomics approach to discover tumor-associated metabolites and metabolic enzymes directly in their native state. A variety of metabolites localized in different metabolic pathways were mapped by airflow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI) in tissues from 256 esophageal cancer patients. In combination with in situ metabolomics analysis, this method provided clues into tumor-associated metabolic pathways, including proline biosynthesis, glutamine metabolism, uridine metabolism, histidine metabolism, fatty acid biosynthesis, and polyamine biosynthesis. Six abnormally expressed metabolic enzymes that are closely associated with the altered metabolic pathways were further discovered in esophageal squamous cell carcinoma (ESCC). Notably, pyrroline-5-carboxylate reductase 2 (PYCR2) and uridine phosphorylase 1 (UPase1) were found to be altered in ESCC. The spatially resolved metabolomics reveal what occurs in cancer at the molecular level, from metabolites to enzymes, and thus provide insights into the understanding of cancer metabolic reprogramming.

scholarly journals Spatial transcriptome profiling by MERFISH reveals subcellular RNA compartmentalization and cell cycle-dependent gene expression

2019 ◽  
Vol 116 (39) ◽  
pp. 19490-19499 ◽  
Author(s):  
Chenglong Xia ◽  
Jean Fan ◽  
George Emanuel ◽  
Junjie Hao ◽  
Xiaowei Zhuang
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Spatial Information ◽  
Detection Efficiency ◽  
Expression Profiles ◽  
Regulation Of Gene Expression ◽  
Rna Profiling ◽  
Spatially Resolved ◽  
Cycle Dependent

The expression profiles and spatial distributions of RNAs regulate many cellular functions. Image-based transcriptomic approaches provide powerful means to measure both expression and spatial information of RNAs in individual cells within their native environment. Among these approaches, multiplexed error-robust fluorescence in situ hybridization (MERFISH) has achieved spatially resolved RNA quantification at transcriptome scale by massively multiplexing single-molecule FISH measurements. Here, we increased the gene throughput of MERFISH and demonstrated simultaneous measurements of RNA transcripts from ∼10,000 genes in individual cells with ∼80% detection efficiency and ∼4% misidentification rate. We combined MERFISH with cellular structure imaging to determine subcellular compartmentalization of RNAs. We validated this approach by showing enrichment of secretome transcripts at the endoplasmic reticulum, and further revealed enrichment of long noncoding RNAs, RNAs with retained introns, and a subgroup of protein-coding mRNAs in the cell nucleus. Leveraging spatially resolved RNA profiling, we developed an approach to determine RNA velocity in situ using the balance of nuclear versus cytoplasmic RNA counts. We applied this approach to infer pseudotime ordering of cells and identified cells at different cell-cycle states, revealing ∼1,600 genes with putative cell cycle-dependent expression and a gradual transcription profile change as cells progress through cell-cycle stages. Our analysis further revealed cell cycle-dependent and cell cycle-independent spatial heterogeneity of transcriptionally distinct cells. We envision that the ability to perform spatially resolved, genome-wide RNA profiling with high detection efficiency and accuracy by MERFISH could help address a wide array of questions ranging from the regulation of gene expression in cells to the development of cell fate and organization in tissues.

scholarly journals Closed system RT-qPCR as a potential companion diagnostic test for immunotherapy outcome in metastatic melanoma

2019 ◽  
Vol 7 (1) ◽  
Author(s):  
Swati Gupta ◽  
Leena McCann ◽  
Yvonne G. Y. Chan ◽  
Edwin W. Lai ◽  
Wei Wei ◽  
...  
Keyword(s):  
Metastatic Melanoma ◽  
Diagnostic Test ◽  
Closed System ◽  
Companion Diagnostic

scholarly journals Proof of Concept to Clinical Confirmation: Evolving Clinical Trial Designs for Targeted Agents

ISRN Oncology ◽  
2012 ◽  
Vol 2012 ◽  
pp. 1-6
Author(s):  
Laura Finn ◽  
Winston Tan
Keyword(s):  
Clinical Trial ◽  
Personalized Medicine ◽  
Clinical Outcomes ◽  
Clinical Characteristics ◽  
Response To Therapy ◽  
Targeted Agents ◽  
Proof Of Concept ◽  
New Agents ◽  
Clinical Trial Designs ◽  
Tumor Types

No single therapy benefits the majority of patients in the practice of oncology as responses differ even among patients with similar tumor types. The variety of response to therapy witnessed while treating our patients supports the concept of personalized medicine using patients' genomic and biologic information and their clinical characteristics to make informed decisions about their treatment. Personalized medicine relies on identification and confirmation of biologic targets and development of agents to target them. These targeted agents tend to focus on subsets of patients and provide improved clinical outcomes. The continued success of personalized medicine will depend on the expedited development of new agents from proof of concept to confirmation of clinical efficacy.

scholarly journals Suppression of cancer relapse and metastasis by inhibiting cancer stemness

2015 ◽  
Vol 112 (6) ◽  
pp. 1839-1844 ◽  
Author(s):  
Youzhi Li ◽  
Harry A. Rogoff ◽  
Sarah Keates ◽  
Yuan Gao ◽  
Sylaja Murikipudi ◽  
...  
Keyword(s):  
Cancer Cells ◽  
Cancer Therapeutics ◽  
Therapeutic Approaches ◽  
Cancer Treatments ◽  
Cancer Stemness ◽  
Recurrent Cancer ◽  
Cancer Relapse ◽  
Novel Approach ◽  
Cancer Types ◽  
Tumor Types

Partial or even complete cancer regression can be achieved in some patients with current cancer treatments. However, such initial responses are almost always followed by relapse, with the recurrent cancer being resistant to further treatments. The discovery of therapeutic approaches that counteract relapse is, therefore, essential for advancing cancer medicine. Cancer cells are extremely heterogeneous, even in each individual patient, in terms of their malignant potential, drug sensitivity, and their potential to metastasize and cause relapse. Indeed, hypermalignant cancer cells, termed cancer stem cells or stemness-high cancer cells, that are highly tumorigenic and metastatic have been isolated from cancer patients with a variety of tumor types. Moreover, such stemness-high cancer cells are resistant to conventional chemotherapy and radiation. Here we show that BBI608, a small molecule identified by its ability to inhibit gene transcription driven by Stat3 and cancer stemness properties, can inhibit stemness gene expression and block spherogenesis of or kill stemness-high cancer cells isolated from a variety of cancer types. Moreover, cancer relapse and metastasis were effectively blocked by BBI608 in mice. These data demonstrate targeting cancer stemness as a novel approach to develop the next generation of cancer therapeutics to suppress cancer relapse and metastasis.

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