Results 61 to 70 of about 71,340 (293)

Spatially resolved transcriptomic profiling of degraded and challenging fresh frozen samples

Nature Communications, 2023
Spatial transcriptomics relies on RNA quality, which is variable and dependent on sample handling, storage, and/or intrinsic factors. Here, authors present a genome-wide spatial gene expression profiling method called RNA Rescue Spatial Transcriptomics ...
Reza Mirzazadeh, Zaneta Andrusivova, Ludvig Larsson, Phillip T. Newton, Leire Alonso Galicia, Xesús M. Abalo, Mahtab Avijgan, Linda Kvastad, Alexandre Denadai-Souza, Nathalie Stakenborg, Alexandra B. Firsova, Alia Shamikh, Aleksandra Jurek, Niklas Schultz, Monica Nistér, Christos Samakovlis, Guy Boeckxstaens, Joakim Lundeberg   +17 more
doaj   +1 more source

Spatial Transcriptomics for Tumor Heterogeneity Analysis

Frontiers in Genetics, 2022
The molecular heterogeneity of cancer is one of the major causes of drug resistance that leads to treatment failure. Thus, better understanding the heterogeneity of cancer will contribute to more precise diagnosis and improved patient outcomes.
Qiongyu Li, Xinya Zhang, Rongqin Ke
doaj   +1 more source

Deciphering transcriptional plasticity in pancreatic ductal adenocarcinoma reveals alterations in sensory neuron innervation

Molecular Oncology, EarlyView.
Pancreatic sensory neurons innervating healthy and PDAC tissue were retrogradely labeled and profiled by single‐cell RNA sequencing. Tumor‐associated innervation showed a dominant neurofilament‐positive subtype, altered mitochondrial gene signatures, and reduced non‐peptidergic neurons.
Elena Genova, Michele Montrone, Uday Rangaswamy, Francesco Diversi, Irene Schiavo, Denise Ferrarini, Roberta Di Florio, Irene Longo, Michele Coscia, Nicola Zamboni, Giorgia Demontis, Lisa Veghini, Vincenzo Corbo, Remo Sanges, Paul Heppenstall   +14 more
wiley   +1 more source

Combining spatial transcriptomics with tissue morphology

Nature Communications
Spatial transcriptomics has transformed our understanding of tissue architecture by preserving the spatial context of gene expression patterns. Simultaneously, advances in imaging AI have enabled extraction of morphological features describing the tissue.
Eduard Chelebian, Christophe Avenel, Carolina Wählby   +2 more
doaj   +1 more source

Nova-ST: Nano-patterned ultra-dense platform for spatial transcriptomics

Cell Reports: Methods
Summary: Spatial transcriptomics workflows using barcoded capture arrays are commonly used for resolving gene expression in tissues. However, existing techniques are either limited by capture array density or are cost prohibitive for large-scale atlasing.
Suresh Poovathingal, Kristofer Davie, Lars E. Borm, Roel Vandepoel, Nicolas Poulvellarie, Annelien Verfaillie, Nikky Corthout, Stein Aerts   +7 more
doaj   +1 more source

Integration of Computational Analysis and Spatial Transcriptomics in Single-cell Studies

Genomics, Proteomics & Bioinformatics, 2023
Recent advances of single-cell transcriptomics technologies and allied computational methodologies have revolutionized molecular cell biology. Meanwhile, pioneering explorations in spatial transcriptomics have opened up avenues to address fundamental ...
Ran Wang, Guangdun Peng, Patrick P.L. Tam, Naihe Jing   +3 more
doaj   +1 more source

Polymicrobial oral biofilm models: simplifying the complex [PDF]

, 2019
Over the past century, numerous studies have used oral biofilm models to investigate growth kinetics, biofilm formation, structure and composition, antimicrobial susceptibility and host–pathogen interactions.
Appelbaum, Black, Bradshaw, Bryn Short, Ceri, Christopher Delaney, Darveau, Fitzgerald, Gibbons, Gordon Ramage, Hamilton, Jason L. Brown, John Butcher, Lie, Loesche, Loesche, Marcello Riggio, Mark C. Butcher, Marsh, Marsh, McKee, Miller, Orstavik, Pratten, Russell, Shauna Culshaw, Sudo, Sudo, Tracy Young, William Johnston   +29 more
core   +3 more sources

Differential expression of cancer‐related genes supports prediction of poor response to first‐line treatments in T‐ALL pediatric patients with high minimal residual disease

Molecular Oncology, EarlyView.
In the present work, we have identified a transcriptional signature based on the differential expression of six genes (BCL2&MAST4, HSH2D&LAT2, METRN&PITPNM2) that would facilitate the early detection of T‐cell acute lymphoblastic leukemia (T‐ALL) patients prone to a poor treatment response and could be implemented at diagnosis, along with other risk ...
Antonio Lahera, Laura Vela‐Martín, Pablo Fernández‐Navarro, José Luis López‐Lorenzo, Javier Cornago, Pilar Llamas, Eduardo Pérez‐Gómez, José Luis Marín‐Rubio, Manuel Fresno, Javier Santos, José Fernández‐Piqueras, María Villa‐Morales   +11 more
wiley   +1 more source

HiSTaR: identifying spatial domains with hierarchical spatial transcriptomics variational autoencoder

Journal of Translational Medicine
Background The development of spatial transcriptomics (ST) has enabled biologists to measure transcriptome data on an entire tissue and retain spatial information.
Junhua Yu, Jiaqi Yuan, Qianbei Yi, Zheng Ye, Peng Xu, Wenbin Liu   +5 more
doaj   +1 more source

Rosetta Brains: A Strategy for Molecularly-Annotated Connectomics [PDF]

, 2014
We propose a neural connectomics strategy called Fluorescent In-Situ Sequencing of Barcoded Individual Neuronal Connections (FISSEQ-BOINC), leveraging fluorescent in situ nucleic acid sequencing in fixed tissue (FISSEQ).
Boyden, Edward S, Church, George M, Daugharthy, Evan R, Kalhor, Reza, Kebschull, Justus M, Kording, Konrad P, Lee, Je Hyuk, Marblestone, Adam H, Mishchenko, Yuriy, Peikon, Ian D, Shipman, Seth L, Zador, Anthony M   +11 more
core