We present a systematic evaluation of state-of-the-art algorithms for inferring gene regulatory networks from single-cell transcriptional data. As the ground truth for assessing accuracy, we use ...synthetic networks with predictable trajectories, literature-curated Boolean models and diverse transcriptional regulatory networks. We develop a strategy to simulate single-cell transcriptional data from synthetic and Boolean networks that avoids pitfalls of previously used methods. Furthermore, we collect networks from multiple experimental single-cell RNA-seq datasets. We develop an evaluation framework called BEELINE. We find that the area under the precision-recall curve and early precision of the algorithms are moderate. The methods are better in recovering interactions in synthetic networks than Boolean models. The algorithms with the best early precision values for Boolean models also perform well on experimental datasets. Techniques that do not require pseudotime-ordered cells are generally more accurate. Based on these results, we present recommendations to end users. BEELINE will aid the development of gene regulatory network inference algorithms.
While the past decade has seen meaningful improvements in clinical outcomes for multiple myeloma patients, a subset of patients does not benefit from current therapeutics for unclear reasons. Many ...gene expression-based models of risk have been developed, but each model uses a different combination of genes and often involves assaying many genes making them difficult to implement. We organized the Multiple Myeloma DREAM Challenge, a crowdsourced effort to develop models of rapid progression in newly diagnosed myeloma patients and to benchmark these against previously published models. This effort lead to more robust predictors and found that incorporating specific demographic and clinical features improved gene expression-based models of high risk. Furthermore, post-challenge analysis identified a novel expression-based risk marker, PHF19, which has recently been found to have an important biological role in multiple myeloma. Lastly, we show that a simple four feature predictor composed of age, ISS, and expression of PHF19 and MMSET performs similarly to more complex models with many more gene expression features included.
BackgroundLung cancers are the leading cause of cancer incidence and mortality with a 5-year survival of ~20%. Treatment with immune checkpoint inhibitors have led to durable and prolonged survival, ...but only a subset of patients remains responsive. Additional biomarkers are needed to better predict which patients will respond to or develop resistance against immune checkpoint inhibitor (ICI) therapies. In this study, we utilize high-dimensional spatial phenotyping of the tumor microenvironment (TME) in Non-small cell lung cancer (NSCLC) to define spatial metrics and spatial scores associated with response and resistance to ICI therapy.MethodsIn this study, we utilized high-plex spatially resolved proteomics to phenotype pre-treatment biopsies from NSCLC patients treated with single-agent Nivolumab. We developed a spatial analysis of an NSCLC cohort using customizable PhenoCode™ Signature Panels (PSP), which combines the barcode-based antibody chemistry from the PhenoCycler® platform with the signal amplification of Opal-TSA chemistry from the PhenoImager® platform. The panels we utilized in this study were for the high-throughput identification of M1/M2 macrophage polarization (CD8, CD68, CD163, PD-1, PD-L1 + PanCK) and immune-contexture (CD3/CD8/CD20/CD68/PanCK + CD4)ResultsOur whole-slide spatially resolved, single-cell phenotyping revealed multiple quantifiable and spatial signatures and scores that are predictive of treatment benefit. The spatial score measured by the ratio of the physical distance between CD8+ T cells and the nearest tumor cell, relative to its nearest macrophage, was significantly higher in the non-responder group when compared to the responder group (p ≤0.0001). Further analysis revealed the polarization state of the macrophages (M1/M2) and how it aids in defining TME features associated with response to therapy.ConclusionsThere is an increasing need for the development of predictive biomarkers of response to ICI therapy. We demonstrate the complementary use of PhenoCode Signature panels high-throughput multiplex imaging for the identification of new spatial signatures within the immune landscape of NSCLC. These methods are amenable to revealing novel cell types and spatial signatures that can be associated with ICI outcomes and therefore can serve as clinical biomarkers.Ethics ApprovalLung: This study has Metro South Human Research Ethics Committee (HREC) Approval (LNR/2019/QMS/51117) and University of Queensland ethics ratification.
BackgroundCutaneous squamous cell carcinoma (cSCC) is the second most common skin cancer, accounting for 75% of deaths due to skin cancer, excluding melanoma. Although prognoses are typically ...favorable, metastasis and resistance to immune checkpoint inhibitors pose an emerging threat. There are few biomarkers that can reliably aid in patient stratification, thus multiplex strategies are required to understand the dynamics of tumor resistance and immune regulation over the course of therapy. Here, we aim to track the tumor microenvironment (TME) dynamics over the course of immune checkpoint inhibitor therapy (ICI).MethodsOur study investigates the spatial TME landscapes of cSCC tissues from patients over the course of immunotherapy treatment. An ultrahigh-plex antibody panel encompassing cell lineages, activation states, immune checkpoints, structural and metabolic markers was deployed on the PhenoCycler®-Fusion spatial biology platform for analysis of the tissue microenvironment. Deep analysis was performed to identify the cellular phenotypes, spatial neighborhoods and functional states driving tumor pathogenesis and response to immunotherapy.ResultsOur data reveals dynamic changes in the TME phenotype compositions, metabolic states, and spatial signatures over the course of therapy. In patients with resistant disease, metabolic features were observed, specifically within the tumor compartments. Overall, ultrahigh-plex phenotyping of the tumor landscape in cSCC reveals novel insights into the key cellular and molecular determinants of tumor biology and clinical outcome.ConclusionsHere, using a multiplex spatio-temporal approach with protein-based spatial phenotyping, we interrogated the pathobiology of cSCC to identify novel biomarkers of metastasis and resistance for patient stratification and therapeutic intervention.
BackgroundBy integrating genomics, transcriptomics, proteomics, and metabolomics, researchers have gained unprecedented insights into the complex interplay between tumors and the immune system. This ...wealth of data has fueled the discovery of novel biomarkers for early cancer detection, prognostic stratification, and prediction of treatment response. Ultrahigh-plex multispectral immunohistochemistry enables the ability to analyze a large set of biomarkers simultaneously while maintaining the context of tissue architecture. By obtaining spatial information, researchers can understand the contact interactions and proximity between different cell types, leading to a deeper understanding of cellular dynamics and disease progression. This approach enables the identification of complex cellular patterns, such as immune cell infiltration, cell-cell interactions, or tumor heterogeneity. Ultimately, these findings may contribute to the development of more effective therapies and diagnostics for cancer patients.MethodsWe performed whole-slide spatial phenotyping with an ultrahigh-plex PhenoCycler-Fusion panel on FFPE human head and neck tumor biopsies (pre-treatment and post-treatment) from a patient receiving immunotherapy. Enumeration of cell phenotypes and spatial location analysis was performed using QuPath,1 R, and proprietary software.ResultsWithin the invasive margin of the tumor (+/- 20mm of the tumor edge) we measured baseline densities of CD8+ T cells (264 cells/mm2) and CD4+ T cells (458 cells/mm2) at the pretreatment timepoint, with the densities substantially increased following treatment (1042 cells/mm2 CD8; 652 cells/mm2 CD4). The CD8+ T cells present within the invasive margin also displayed functionality as evidenced by their expression of GranzymeB (109 vs 397 cells/mm2, pre vs post). In addition, differential enrichment was observed within the invasive margin compared to the entire biopsy (41% vs 58%, pre vs post). In conjunction tumor cells at the post-treatment timepoint showed higher expression of the pro-apoptotic BAX protein (34% vs 58%, pre vs post) with reduced expression of the anti-apoptotic Bcl-2 protein (32% vs 12%, pre vs post).ConclusionsData from this ultrahigh-plex multispectral panel has provided a number of potential biomarkers for investigation in patients with head and neck cancer receiving immunotherapy. These findings inform development of smaller, more efficient multispectral panels to be designed and run on larger cohorts of patients. Additionally, examining cell populations residing within specific spatial tumor microenvironments (ie., invasive margin) uncovers additional metrics that should be examined in association with a patient’s response to treatment.AcknowledgementsSupport: Providence Medical Foundation, The Chiles Foundation, The Harder Family, Nancy Lematta, Robert W. Franz and Elsie Franz Finley, The Murdoch Memorial TrustReferenceBankhead P, et al. QuPath: Open source software for digital pathology image analysis. Scientific Reports 2017.Ethics ApprovalPSJH IRB# 06–108
Image-based cell phenotyping with deep learning Pratapa, Aditya; Doron, Michael; Caicedo, Juan C.
Current opinion in chemical biology,
December 2021, 2021-12-00, 20211201, Letnik:
65
Journal Article
Recenzirano
A cell's phenotype is the culmination of several cellular processes through a complex network of molecular interactions that ultimately result in a unique morphological signature. Visual cell ...phenotyping is the characterization and quantification of these observable cellular traits in images. Recently, cellular phenotyping has undergone a massive overhaul in terms of scale, resolution, and throughput, which is attributable to advances across electronic, optical, and chemical technologies for imaging cells. Coupled with the rapid acceleration of deep learning–based computational tools, these advances have opened up new avenues for innovation across a wide variety of high-throughput cell biology applications. Here, we review applications wherein deep learning is powering the recognition, profiling, and prediction of visual phenotypes to answer important biological questions. As the complexity and scale of imaging assays increase, deep learning offers computational solutions to elucidate the details of previously unexplored cellular phenotypes.
Synthetic lethal sets are sets of reactions/genes where only the simultaneous removal of all reactions/genes in the set abolishes growth of an organism. Previous approaches to identify synthetic ...lethal genes in genome-scale metabolic networks have built on the framework of flux balance analysis (FBA), extending it either to exhaustively analyze all possible combinations of genes or formulate the problem as a bi-level mixed integer linear programming (MILP) problem. We here propose an algorithm, Fast-SL, which surmounts the computational complexity of previous approaches by iteratively reducing the search space for synthetic lethals, resulting in a substantial reduction in running time, even for higher order synthetic lethals.
We performed synthetic reaction and gene lethality analysis, using Fast-SL, for genome-scale metabolic networks of Escherichia coli, Salmonella enterica Typhimurium and Mycobacterium tuberculosis. Fast-SL also rigorously identifies synthetic lethal gene deletions, uncovering synthetic lethal triplets that were not reported previously. We confirm that the triple lethal gene sets obtained for the three organisms have a precise match with the results obtained through exhaustive enumeration of lethals performed on a computer cluster. We also parallelized our algorithm, enabling the identification of synthetic lethal gene quadruplets for all three organisms in under 6 h. Overall, Fast-SL enables an efficient enumeration of higher order synthetic lethals in metabolic networks, which may help uncover previously unknown genetic interactions and combinatorial drug targets.
The MATLAB implementation of the algorithm, compatible with COBRA toolbox v2.0, is available at https://github.com/RamanLab/FastSL CONTACT: kraman@iitm.ac.in
Supplementary data are available at Bioinformatics online.
Abstract
Motivation
High-quality curation of the proteins and interactions in signaling pathways is slow and painstaking. As a result, many experimentally detected interactions are not annotated to ...any pathways. A natural question that arises is whether or not it is possible to automatically leverage existing pathway annotations to identify new interactions for inclusion in a given pathway.
Results
We present RegLinker, an algorithm that achieves this purpose by computing multiple short paths from pathway receptors to transcription factors within a background interaction network. The key idea underlying RegLinker is the use of regular language constraints to control the number of non-pathway interactions that are present in the computed paths. We systematically evaluate RegLinker and five alternative approaches against a comprehensive set of 15 signaling pathways and demonstrate that RegLinker recovers withheld pathway proteins and interactions with the best precision and recall. We used RegLinker to propose new extensions to the pathways. We discuss the literature that supports the inclusion of these proteins in the pathways. These results show the broad potential of automated analysis to attenuate difficulties of traditional manual inquiry.
Availability and implementation
https://github.com/Murali-group/RegLinker.
Supplementary information
Supplementary data are available at Bioinformatics online.
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