24 papers · ranked by Valyu relevance
Bo Wang, Yahui Long, Yuting Bai, Jiawei Luo + 1 more
In this paper, we propose STCGAN, a cellular deconvolution method based on a cycle-consistent generative adversarial network. We first employ a cycle-consistent adversarial network to capture the complex spatial gene expression patterns, ensuring that the model can accurately depict the spatial structure. Next, we…
Xiangyu Qian, Jing Liu, Yunqing Tang, Luru Dai + 1 more
Fluorescence microscopy images are degraded by noise and diffraction-induced blur, which compromise structural fidelity and limit quantitative analysis. Supervised deep learning methods achieve impressive restoration performance but require large-scale paired datasets that are difficult to obtain in practice. To…
Bernhard Eder, Irene Rigato, Alexander Dietrich, Lorenzo Merotto + 5 more
Bulk RNA-seq enables effective profiling of large cohorts and complex experimental designs, but current single-cell-informed deconvolution methods incompletely resolve closely related cell phenotypes, do not scale efficiently to large single-cell datasets, or fail to account for cellular content not represented in the…
Gabriel Torregrosa, David Oriola, Vikas Trivedi, Jordi García‐Ojalvo
Individual cells exhibit substantial heterogeneity in protein abundance and activity, which is frequently reflected in broad distributions of fluorescently labeled reporters. Since all cellular components are intrinsically fluorescent to some extent, the observed distributions contain background noise that masks the…
Alba Lomas Redondo, Jose M. Sánchez Velázquez, Álvaro J. García Tejedor, Víctor Javier Sánchez–Arévalo Lobo
'Álvaro J. García Tejedor' 'Víctor Javier Sánchez–Arévalo Lobo'] Within this systematic review we examine the role of Artificial Intelligence (AI) and Deep Learning (DL) in the development of cellular deconvolution tools, with an special focus on their application to the analysis of transcriptomics data from RNA…
Fudong Xue, Wenting He, Zuo’ang Xiang, Jun Ren + 3 more
Advancing single-frame imaging techniques beyond the diffraction limit and upgrading traditional wide-field or confocal microscopes to super-resolution (SR) capabilities are greatly sought after by biologists. While enhancing image resolution by deconvolving noise-free images is beneficial, achieving a noise-free image…
Martijn R. Molenaar, Mohammed Shahraz, Jeany Delafiori, Andreas Eisenbarth + 3 more
'Andreas Eisenbarth' 'Måns Ekelöf' 'Luca Rappez' 'Theodore Alexandrov'] Imaging mass spectrometry (MS) is becoming increasingly applied for single-cell analyses. Multiple methods for imaging MS-based single-cell metabolomics were proposed, including our recent method SpaceM. An important step in imaging MS-based…
Sylvain Prigent, Hoai-Nam Nguyen, Ludovic Leconte, Cesar Augusto Valades-Cruz + 3 more
While fluorescent microscopy imaging has become the spearhead of modern biology as it is able to generate long-term videos depicting 4D nanoscale cell behaviors, it is still limited by the optical aberrations and the photon budget available in the specimen and to some extend to photo-toxicity. A direct consequence is…
Yiwei Hou, Wenyi Wang, Yunzhe Fu, Xichuan Ge + 2 more
Despite the grand advances in fluorescence microscopy, the photon budget of fluorescent molecules remains the fundamental limiting factor for major imaging parameters, such as temporal resolution, duration, contrast, and even spatial resolution. Computational methods can strategically utilize the fluorescence photons…
Eilis Hannon, Emma L. Dempster, Jonathan P. Davies, Barry Chioza + 8 more
Background Due to interindividual variation in the cellular composition of the human cortex, it is essential that covariates that capture these differences are included in epigenome-wide association studies using bulk tissue. As experimentally derived cell counts are often unavailable, computational solutions have been…
Bastien Chassagnol, Grégory Nuel, Étienne Becht
Although bulk transcriptomic analyses have greatly contributed to a better understanding of complex diseases, their sensibility is hampered by the highly heterogeneous cellular compositions of biological samples. To address this limitation, computational deconvolution methods have been designed to automatically…
Bastien Chassagnol, Grégory Nuel, Étienne Becht
Although bulk transcriptomic analyses have significantly contributed to an enhanced comprehension of multifaceted diseases, their exploration capacity is impeded by the heterogeneous compositions of biological samples. Indeed, by averaging expression of multiple cell types, RNA-Seq analysis is oblivious to variations…
Erik Wernersson, Eleni Gelali, Gabriele Girelli, Su Wang + 13 more
'David Castillo' 'Christoffer Mattsson Langseth' 'Quentin Verron' 'Huy Q. Nguyen' 'Shyamtanu Chattoraj' 'Anna Martinez Casals' 'Hans Blom' 'Emma Lundberg' 'Mats Nilsson' 'Marc A. Marti-Renom' 'Chao-ting Wu' 'Nicola Crosetto' 'Magda Bienko'] Microscopy-based spatially resolved omic methods are transforming the life…
Qianhui Huang, Yijun Li, Chuan Xu, Sarah A. Teichmann + 5 more
'Naftali Kaminski' 'Matteo Pellegrini' 'Quan Dong Nguyen' 'Andrew E. Teschendorff' 'Lana X. Garmire'] Deciphering cell type heterogeneity is crucial for systematically understanding tissue homeostasis and its dysregulation in diseases. Computational deconvolution is an efficient approach estimating cell type abundances…
Bin Fu, Caroline L. Jones, Daniel Heraghty, Shengbo Yang + 8 more
Imaging flow cytometry using Fourier light-field microscopy enables high-throughput three-dimensional cellular imaging, capable of capturing thousands of events per second. However, volumetric reconstruction speed remains orders of magnitude slower than the acquisition speed. The current state of art uses…
Haohong Gan, Shiyi Peng, Hailian Hu, Xuan You + 4 more
The resolving power of optical microscopy is fundamentally constrained by the diffraction of light, limiting our ability to visualize subcellular structures. Computational methods, particularly deconvolution, can restore blurred images but critically depend on an accurate point spread function (PSF), whose estimation…
Alba Lomas Redondo, Jose M. Sánchez Velázquez, Álvaro J. García Tejedor, Víctor Javier Sánchez–Arévalo
Within this systematic review we examine the role of Artificial Intelligence (AI) and Deep Learning (DL) in the development of cellular deconvolution tools, with an special focus on their application to the analysis of transcriptomics data from RNA sequencing. We emphasize the critical importance of high–quality…
Baoliang Ge, Yanping He, Mo Deng, Md Habibur Rahman + 10 more
'Ziling Wu' 'Chung Hong N. Wong' 'Michael K. Chan' 'Yi‐Ping Ho' 'Liting Duan' 'Zahid Yaqoob' 'Peter T. C. So' 'George Barbastathis' 'Renjie Zhou'] 1 Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA 2 Laser Biomedical Research Center, Massachusetts Institute of…
Authors not listed
Desorption Electrospray Ionization Mass Spectrometry Imaging (DESI-MSI) is a powerful technique for molecular analysis of surfaces; however, its application of single cell studies has not been previously published. In the current work, a commercial DESI setup (DESI XS) coupled to a mass spectrometer was used to analyze…
Adeleke Maradesa, Baptiste Py, Ting Hei Wan, Mohammed B. Effat + 1 more
Electrochemical impedance spectroscopy (EIS) is a characterization technique used widely in electrochemistry. Obtaining EIS data is simple when modern electrochemical workstations are used; however, analyzing EIS spectra is still a considerable quandary. The distribution of relaxation times (DRT) has emerged as a…
Authors not listed
Scanning ion conductance microscopy (SICM) offers non-contact, label-free imaging of live cells with nanometer-scale resolution. However, its conventional imaging mode is inherently slow due to repeated vertical scanning, limiting temporal resolution and causing inertial issues. Here, we present Scanning Counter Ion…
Denice van Herwerden, Jake O'Brien, Sascha Lege, Bob Pirok + 2 more
Fragment deconvolution is a crucial step during componentization of non-targeted analysis (NTA) high-resolution mass spectrometry (HRMS) data, aiming to filter out false positive (FP) signals that do not belong to the component. Moreover, inclusion of FP fragments could lead to, for example, wrong identification…
Laura Baracaldo, Blythe King, Haoran Yan, Yizi Lin + 2 more
'Mengyang Gu'] Cell boundary information is crucial for analyzing cell behaviors from time-lapse microscopy videos. Existing supervised cell segmentation tools, such as ImageJ, require tuning various parameters and rely on restrictive assumptions about the shape of the objects. While recent supervised segmentation…
Authors not listed
Self-organizing tissues, such as organoids, offer transformative potential beyond healthcare by enabling the sustainable production of advanced materials. Resource scarcity and global warming drive the need for innovative fabrication solutions. This prospective review explores developmental biology as a manufacturing…