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A special issue of Cells (ISSN 2073-4409). This special issue belongs to the section "Cell Microenvironment".

Deadline for manuscript submissions: closed (25 May 2023) | Viewed by 29515

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Special Issue Editors

Prof. Dr. Zhi Wei

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Guest Editor

Department of Computer Science, New Jersey Institute of Technology, Newark, NJ 07102, USA
Interests: bioinformatics; computational genomics; statistical modeling

Dr. Turki Turki

E-Mail Website
Guest Editor

Department of Computer Sciences, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia Kingdom
Interests: bioinformatics; computational genomics; statistical modeling

Special Issue Information

Dear Colleagues,

Stupendous data of different formats generated via high-throughput technologies constitute a limitless opportunity to develop computational methods to perform data analysis. These bioinformatics methods and tools will help to address important (and new) biological problems for understanding the working mechanism of cells. Such data analysis aims to (1) identify cancer cells that are sensitive or resistant to a given compound; (2) rank effective compounds per cancer cell; (3) identify potential compounds for the treatment of cells infected with viruses (e.g., SARS-CoV-2); (4) identify biomarkers that play a key role in the treatment (or progression) of various diseases; and (5) identify infected cells at the tissue level. Although these problems have been recently investigated, but mostly using genomic-based information, the results derived thus far from such computational methods are far from perfect.

This Special Issue aims to address new problems and improve results pertaining to cells using computational methods utilizing not only genomic information but other data formats including (but not limited to) clinical and imaging formats.

Therefore, we welcome the submission of computational methods related, but not limited to, the following:

Molecules;
DNA methylation;
Copy number variation;
Whole exome sequencing;
Gene expression;
RNA sequencing data;
Single-cell RNA-seq data;
Cells;
Tissues;
Cancer;
Viruses;
Diseases;
Systems biology;
Bioinformatics;
Biological interpretation and evaluation;
Machine learning;
Deep learning;
Artificial intelligence.

Prof. Dr. Zhi Wei
Dr. Turki Turki
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cells is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Published Papers (11 papers)

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Research

16 pages, 2657 KiB

Open AccessArticle

SINFONIA: Scalable Identification of Spatially Variable Genes for Deciphering Spatial Domains

by Rui Jiang, Zhen Li, Yuhang Jia, Siyu Li and Shengquan Chen

Cells 2023, 12(4), 604; https://doi.org/10.3390/cells12040604 - 13 Feb 2023

Cited by 7 | Viewed by 2567

Abstract

Recent advances in spatial transcriptomics have revolutionized the understanding of tissue organization. The identification of spatially variable genes (SVGs) is an essential step for downstream spatial domain characterization. Although several methods have been proposed for identifying SVGs, inadequate ability to decipher spatial domains, poor efficiency, and insufficient interoperability with existing standard analysis workflows still impede the applications of these methods. Here we propose SINFONIA, a scalable method for identifying spatially variable genes via ensemble strategies. Implemented in Python, SINFONIA can be seamlessly integrated into existing analysis workflows. Using 15 spatial transcriptomic datasets generated with different protocols and with different sizes, dimensions and qualities, we show the advantage of SINFONIA over three baseline methods and two variants via systematic evaluation of spatial clustering, domain resolution, latent representation, spatial visualization, and computational efficiency with 21 quantitative metrics. Additionally, SINFONIA is robust relative to the choice of the number of SVGs. We anticipate SINFONIA will facilitate the analysis of spatial transcriptomics. Full article

(This article belongs to the Special Issue Bioinformatics and Cells)

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36 pages, 5482 KiB

Open AccessArticle

HGCA2.0: An RNA-Seq Based Webtool for Gene Coexpression Analysis in Homo sapiens

by Vasileios L. Zogopoulos, Apostolos Malatras, Konstantinos Kyriakidis, Chrysanthi Charalampous, Evanthia A. Makrygianni, Stéphanie Duguez, Marianna A. Koutsi, Marialena Pouliou, Christos Vasileiou, William J. Duddy, Marios Agelopoulos, George P. Chrousos, Vassiliki A. Iconomidou and Ioannis Michalopoulos

Cells 2023, 12(3), 388; https://doi.org/10.3390/cells12030388 - 21 Jan 2023

Cited by 1 | Viewed by 2882

Abstract

Genes with similar expression patterns in a set of diverse samples may be considered coexpressed. Human Gene Coexpression Analysis 2.0 (HGCA2.0) is a webtool which studies the global coexpression landscape of human genes. The website is based on the hierarchical clustering of 55,431 Homo sapiens genes based on a large-scale coexpression analysis of 3500 GTEx bulk RNA-Seq samples of healthy individuals, which were selected as the best representative samples of each tissue type. HGCA2.0 presents subclades of coexpressed genes to a gene of interest, and performs various built-in gene term enrichment analyses on the coexpressed genes, including gene ontologies, biological pathways, protein families, and diseases, while also being unique in revealing enriched transcription factors driving coexpression. HGCA2.0 has been successful in identifying not only genes with ubiquitous expression patterns, but also tissue-specific genes. Benchmarking showed that HGCA2.0 belongs to the top performing coexpression webtools, as shown by STRING analysis. HGCA2.0 creates working hypotheses for the discovery of gene partners or common biological processes that can be experimentally validated. It offers a simple and intuitive website design and user interface, as well as an API endpoint. Full article

(This article belongs to the Special Issue Bioinformatics and Cells)

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13 pages, 2516 KiB

Open AccessArticle

Optimization of 3D Immunofluorescence Analysis and Visualization Using IMARIS and MeshLab

by Zulzikry Hafiz Abu Bakar, Jean-Pierre Bellier, Wan Zurinah Wan Ngah, Daijiro Yanagisawa, Ken-ichi Mukaisho and Ikuo Tooyama

Cells 2023, 12(2), 218; https://doi.org/10.3390/cells12020218 - 4 Jan 2023

Cited by 2 | Viewed by 2291

Abstract

The precision of colocalization analysis is enhanced by 3D and is potentially more accurate than 2D. Even though 3D improves the visualization of colocalization analysis, rendering a colocalization model may generate a model with numerous polygons. We developed a 3D colocalization model of FtMt/LC3 followed by simplification. Double immunofluorescence staining of FtMt and LC3 was conducted, and stacked images were acquired. We used IMARIS to render the 3D colocalization model of FtMt/LC3 and further processed it with MeshLab to decimate and generate a less complex colocalization model. We examined the available simplification algorithm using MeshLab in detail and evaluated the feasibility of each procedure in generating a model with less complexity. The quality of the simplified model was subsequently assessed. MeshLab’s available shaders were scrutinized to facilitate the spatial colocalization determination. Finally, we showed that QECD was the most effective method for reducing the polygonal complexity of the colocalization model without compromising its quality. In addition, we would recommend implementing the x-ray shader, which we found useful for visualizing colocalization. As 3D was found to be more accurate in quantifying colocalization, our study provides a novel and dependable method for rendering 3D models for colocalization analysis. Full article

(This article belongs to the Special Issue Bioinformatics and Cells)

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17 pages, 5622 KiB

Open AccessArticle

Multi-Scope Feature Extraction for Intracranial Aneurysm 3D Point Cloud Completion

by Wuwei Ma, Xi Yang, Qiufeng Wang, Kaizhu Huang and Xiaowei Huang

Cells 2022, 11(24), 4107; https://doi.org/10.3390/cells11244107 - 17 Dec 2022

Viewed by 1803

Abstract

3D point clouds are gradually becoming more widely used in the medical field, however, they are rarely used for 3D representation of intracranial vessels and aneurysms due to the time-consuming data reconstruction. In this paper, we simulate the incomplete intracranial vessels (including aneurysms) in the actual collection from different angles, then propose Multi-Scope Feature Extraction Network (MSENet) for Intracranial Aneurysm 3D Point Cloud Completion. MSENet adopts a multi-scope feature extraction encoder to extract the global features from the incomplete point cloud. This encoder utilizes different scopes to fuse the neighborhood information for each point fully. Then a folding-based decoder is applied to obtain the complete 3D shape. To enable the decoder to intuitively match the original geometric structure, we engage the original points coordinates input to perform residual linking. Finally, we merge and sample the complete but coarse point cloud from the decoder to obtain the final refined complete 3D point cloud shape. We conduct extensive experiments on both 3D intracranial aneurysm datasets and general 3D vision PCN datasets. The results demonstrate the effectiveness of the proposed method on three evaluation metrics compared to baseline: our model increases the F-score to

0.379

(

+ 21.1 %

0.320

(

+ 7.7 %

), reduces Chamfer Distance score to

0.998

(

- 33.8 %

0.974

(

- 6.4 %

), and reduces the Earth Mover’s Distance to

2.750

(

17.8 %

2.858

(

- 0.8 %

). Full article

(This article belongs to the Special Issue Bioinformatics and Cells)

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17 pages, 5729 KiB

Open AccessArticle

Inhibition of EZH2 Ameliorates Sepsis Acute Lung Injury (SALI) and Non-Small-Cell Lung Cancer (NSCLC) Proliferation through the PD-L1 Pathway

by Ziyi Wang, Zhe Guo, Xuesong Wang, Haiyan Liao, Yan Chai, Ziwen Wang and Zhong Wang

Cells 2022, 11(24), 3958; https://doi.org/10.3390/cells11243958 - 7 Dec 2022

Cited by 4 | Viewed by 2092

Abstract

(1) Background: Both sepsis acute lung injury (SALI) and non-small-cell lung cancer (NSCLC) are life-threatening diseases caused by immune response disorders and inflammation, but the underlining linking mechanisms are still not clear. This study aimed to detect the shared gene signature and potential molecular process between SALI and NSCLC. (2) Methods: RNA sequences and patient information on sepsis and NSCLC were acquired from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was used to build a co-expression network associated with sepsis and NSCLC. Protein–protein interaction (PPI) analysis of shared genes was intuitively performed by the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database. The involvement of EZH2 in the tumor immune microenvironment (TIME) and sepsis immune microenvironment (IME) was assessed by R software. Western blot, flow cytometry, and other in vitro assays were performed to further confirm the function and mechanism of EZH2 in NSCLC and SALI. (3) Results: WGCNA recognized three major modules for sepsis and two major modules for NSCLC, and there were seven shared genes identified for the two diseases. Additionally, the hub gene EZH2 was screened out. It was shown that EZH2 was closely related to the IME in the two diseases. In the validation assay, our data showed that EZH2 was expressed at a higher level in peripheral blood mononuclear cells (PBMCs) of septic patients than those of healthy donors (HDs), and EZH2 was also expressed at a higher level in lipopolysaccharide (LPS)-induced PBMCs and non-small cell lung cancer (A549) cells. EZH2 inhibitor (GSK343) downregulated the proliferation ability of A549 cells in a concentration-dependent manner, parallel with the decreased expression level of PD-L1. Similarly, GSK343 inhibited PD-L1 protein expression and downregulated the level of proinflammatory factors in LPS-induced PBMCs. In the co-culture system of PBMCs and human type II alveolar epithelial cells (ATIIs), the addition of GSK343 to PBMCs significantly downregulated the apoptosis of LPS-induced ATIIs. (4) Conclusions: This study illustrated that EZH2 inhibition could ameliorate A549 cell proliferation and LPS-induced ATII apoptosis in parallel with downregulation of PD-L1 protein expression, which provided new insights into molecular signaling networks involved in the pathogenetics of SALI and NSCLC. Full article

(This article belongs to the Special Issue Bioinformatics and Cells)

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24 pages, 3385 KiB

Open AccessArticle

Toward Precision Radiotherapy: A Nonlinear Optimization Framework and an Accelerated Machine Learning Algorithm for the Deconvolution of Tumor-Infiltrating Immune Cells

by Lois Chinwendu Okereke, Abdulmalik Usman Bello and Emmanuel Akwari Onwukwe

Cells 2022, 11(22), 3604; https://doi.org/10.3390/cells11223604 - 14 Nov 2022

Cited by 1 | Viewed by 1578

Abstract

Tumor-infiltrating immune cells (TIICs) form a critical part of the ecosystem surrounding a cancerous tumor. Recent advances in radiobiology have shown that, in addition to damaging cancerous cells, radiotherapy drives the upregulation of immunosuppressive and immunostimulatory TIICs, which in turn impacts treatment response. Quantifying TIICs in tumor samples could form an important predictive biomarker guiding patient stratification and the design of radiotherapy regimens and combined immune-radiation treatments. As a result of several limitations associated with experimental methods for quantifying TIICs and the availability of extensive gene sequencing data, deconvolution-based computational methods have appeared as a suitable alternative for quantifying TIICs. Accordingly, we introduce and discuss a nonlinear regression approach (remarkably different from the traditional linear modeling approach of current deconvolution-based methods) and a machine learning algorithm for approximating the solution of the resulting constrained optimization problem. This way, the deconvolution problem is treated naturally, given that the gene expression levels of pure and heterogenous samples do not have a strictly linear relationship. When applied across transcriptomics datasets, our approach, which also allows the coupling of different loss functions, yields results that closely match ground-truth values from experimental methods and exhibits superior performance over popular deconvolution-based methods. Full article

(This article belongs to the Special Issue Bioinformatics and Cells)

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24 pages, 5492 KiB

Open AccessArticle

Global Transcriptional and Epigenetic Reconfiguration during Chemical Reprogramming of Human Retinal Pigment Epithelial Cells into Photoreceptor-like Cells

by Xiaoqian Deng, Ryan Lee, Sin Yee Lim, Zheng Zhong, Jing Wang, Yizhi Liu and Guoping Fan

Cells 2022, 11(19), 3146; https://doi.org/10.3390/cells11193146 - 6 Oct 2022

Cited by 1 | Viewed by 2793

Abstract

Retinal degenerative diseases are frequently caused by the loss of retinal neural cells such as photoreceptors. Cell replacement is regarded as one of the most promising therapies. Multiple types of stem and somatic cells have been tested for photoreceptor conversion. However, current induction efficiencies are still low and the molecular mechanisms underlying reprogramming remain to be clarified. In this work, by combining treatment with small molecules, we directly reprogrammed human fetal retinal pigment epithelial (RPE) cells into chemically induced photoreceptor-like cells (CiPCs) in vitro. Bulk and single-cell RNA sequencing, as well as methylation sequencing, were performed to understand the transcriptional and epigenetic changes during CiPCs conversion. A multi-omics analysis showed that the direct reprogramming process partly resembled events of early retina development. We also found that the efficiency of CiPCs conversion from RPE is much better than that from human dermal fibroblasts (HDF). The small molecules effectively induced RPE cells into CiPCs via suppression of the epithelial-to-mesenchymal transition (EMT). Among the signaling pathways involved in CiPCs conversion, glutamate receptor activation is prominent. In summary, RPE cells can be efficiently reprogrammed into photoreceptor-like cells through defined pharmacological modulations, providing a useful cell source for photoreceptor generation in cell replacement therapy for retinal degenerative diseases. Full article

(This article belongs to the Special Issue Bioinformatics and Cells)

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13 pages, 3229 KiB

Open AccessArticle

Single Cell Analysis Reveals Reciprocal Tumor-Macrophage Intercellular Communications Related with Metabolic Reprogramming in Stem-like Gastric Cancer

by Ji-Yong Sung and Jae-Ho Cheong

Cells 2022, 11(15), 2373; https://doi.org/10.3390/cells11152373 - 2 Aug 2022

Cited by 3 | Viewed by 2994

Abstract

Metabolic alterations and direct cell–cell interactions in the tumor microenvironment (TME) affect the prognostic molecular landscape of tumors; thus, it is imperative to investigate metabolic activity at the single-cell level rather than in bulk samples to understand the high-resolution mechanistic influences of cell-type specific metabolic pathway alterations on tumor cells. To investigate tumor metabolic reprogramming and intercellular communication at the single-cell level, we analyzed eighty-four metabolic pathways, seven metabolic signatures, and tumor-stroma cell interaction using 21,084 cells comprising gastric cancer and paired normal tissue. High EMT-score cells and stem-like subtype tumors showed elevated glycosaminoglycan metabolism, which was associated with poor patient outcome. Adenocarcinoma and macrophage cells had higher reactive oxidative species levels than the normal controls; they largely constituted the highest stemness cluster. They were found to reciprocally communicate through the common ligand RPS19. Consequently, ligand-target regulated transcriptional reprogramming resulted in HS6ST2 expression in adenocarcinoma cells and SERPINE1 expression in macrophages. Gastric cancer patients with increased SERPINE1 and HS6ST2 expression had unfavorable prognoses, suggesting these as potential drug targets. Our findings indicate that malignant stem-like/EMT cancer cell state might be regulated through reciprocal cancer cell-macrophage intercellular communication and metabolic reprogramming in the heterogeneous TME of gastric cancer at the single-cell level. Full article

(This article belongs to the Special Issue Bioinformatics and Cells)

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12 pages, 2337 KiB

Open AccessArticle

ChromoEnhancer: An Artificial-Intelligence-Based Tool to Enhance Neoplastic Karyograms as an Aid for Effective Analysis

by Yahya Bokhari, Areej Alhareeri, Abdulrhman Aljouie, Aziza Alkhaldi, Mamoon Rashid, Mohammed Alawad, Raghad Alhassnan, Saad Samargandy, Aliakbar Panahi, Wolfgang Heidrich and Tomasz Arodz

Cells 2022, 11(14), 2244; https://doi.org/10.3390/cells11142244 - 20 Jul 2022

Cited by 5 | Viewed by 2852

Abstract

Cytogenetics laboratory tests are among the most important procedures for the diagnosis of genetic diseases, especially in the area of hematological malignancies. Manual chromosomal karyotyping methods are time consuming and labor intensive and, hence, expensive. Therefore, to alleviate the process of analysis, several attempts have been made to enhance karyograms. The current chromosomal image enhancement is based on classical image processing. This approach has its limitations, one of which is that it has a mandatory application to all chromosomes, where customized application to each chromosome is ideal. Moreover, each chromosome needs a different level of enhancement, depending on whether a given area is from the chromosome itself or it is just an artifact from staining. The analysis of poor-quality karyograms, which is a difficulty faced often in preparations from cancer samples, is time consuming and might result in missing the abnormality or difficulty in reporting the exact breakpoint within the chromosome. We developed ChromoEnhancer, a novel artificial-intelligence-based method to enhance neoplastic karyogram images. The method is based on Generative Adversarial Networks (GANs) with a data-centric approach. GANs are known for the conversion of one image domain to another. We used GANs to convert poor-quality karyograms into good-quality images. Our method of karyogram enhancement led to robust routine cytogenetic analysis and, therefore, to accurate detection of cryptic chromosomal abnormalities. To evaluate ChromoEnahancer, we randomly assigned a subset of the enhanced images and their corresponding original (unenhanced) images to two independent cytogeneticists to measure the karyogram quality and the elapsed time to complete the analysis, using four rating criteria, each scaled from 1 to 5. Furthermore, we compared the enhanced images with our method to the original ones, using quantitative measures (PSNR and SSIM metrics). Full article

(This article belongs to the Special Issue Bioinformatics and Cells)

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21 pages, 3861 KiB

Open AccessArticle

The Functional Interaction of EGFR with AT1R or TP in Primary Vascular Smooth Muscle Cells Triggers a Synergistic Regulation of Gene Expression

by Virginie Dubourg, Barbara Schreier, Gerald Schwerdt, Sindy Rabe, Ralf A. Benndorf and Michael Gekle

Cells 2022, 11(12), 1936; https://doi.org/10.3390/cells11121936 - 16 Jun 2022

Cited by 6 | Viewed by 1942

Abstract

In vivo, cells are simultaneously exposed to multiple stimuli whose effects are difficult to distinguish. Therefore, they are often investigated in experimental cell culture conditions where stimuli are applied separately. However, it cannot be presumed that their individual effects simply add up. As a proof-of-principle to address the relevance of transcriptional signaling synergy, we investigated the interplay of the Epidermal Growth Factor Receptor (EGFR) with the Angiotensin-II (AT1R) or the Thromboxane-A2 (TP) receptors in murine primary aortic vascular smooth muscle cells. Transcriptome analysis revealed that EGFR-AT1R or EGFR-TP simultaneous activations led to different patterns of regulated genes compared to individual receptor activations (qualitative synergy). Combined EGFR-TP activation also caused a variation of amplitude regulation for a defined set of genes (quantitative synergy), including vascular injury-relevant ones (Klf15 and Spp1). Moreover, Gene Ontology enrichment suggested that EGFR and TP-induced gene expression changes altered processes critical for vascular integrity, such as cell cycle and senescence. These bioinformatics predictions regarding the functional relevance of signaling synergy were experimentally confirmed. Therefore, by showing that the activation of more than one receptor can trigger a synergistic regulation of gene expression, our results epitomize the necessity to perform comprehensive network investigations, as the study of individual receptors may not be sufficient to understand their physiological or pathological impact. Full article

(This article belongs to the Special Issue Bioinformatics and Cells)

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15 pages, 2443 KiB

Open AccessArticle

Predicting CRISPR/Cas9 Repair Outcomes by Attention-Based Deep Learning Framework

by Xiuqin Liu, Shuya Wang and Dongmei Ai

Cells 2022, 11(11), 1847; https://doi.org/10.3390/cells11111847 - 5 Jun 2022

Cited by 8 | Viewed by 3668

Abstract

As a simple and programmable nuclease-based genome editing tool, the CRISPR/Cas9 system has been widely used in target-gene repair and gene-expression regulation. The DNA mutation generated by CRISPR/Cas9-mediated double-strand breaks determines its biological and phenotypic effects. Experiments have demonstrated that CRISPR/Cas9-generated cellular-repair outcomes depend on local sequence features. Therefore, the repair outcomes after DNA break can be predicted by sequences near the cleavage sites. However, existing prediction methods rely on manually constructed features or insufficiently detailed prediction labels. They cannot satisfy clinical-level-prediction accuracy, which limit the performance of these models to existing knowledge about CRISPR/Cas9 editing. We predict 557 repair labels of DNA, covering the vast majority of Cas9-generated mutational outcomes, and build a deep learning model called Apindel, to predict CRISPR/Cas9 editing outcomes. Apindel, automatically, trains the sequence features of DNA with the GloVe model, introduces location information through Positional Encoding (PE), and embeds the trained-word vector matrixes into a deep learning model, containing BiLSTM and the Attention mechanism. Apindel has better performance and more detailed prediction categories than the most advanced DNA-mutation-predicting models. It, also, reveals that nucleotides at different positions relative to the cleavage sites have different influences on CRISPR/Cas9 editing outcomes. Full article

(This article belongs to the Special Issue Bioinformatics and Cells)

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