Biology subjects

Balassa, T.

Publications and source records attributed to Balassa, T..

6 recordsLinked to original sources

bioRxiv2022

Probe set selection for targeted spatial transcriptomics

Targeted spatial transcriptomics methods capture the topology of cell types and states in tissues at single cell- and subcellular resolution by measuring the expression of a predefined set of genes. The selection of an optimal set of probed genes is crucial for capturing and interpreting the spatial signals present in a tissue. However, current selections often rely on marker genes, precluding them from detecting continuous spatial signals or novel states. We present Spapros, an end-to-end probe set selection pipeline that optimizes both probe set specificity for cell type identification and within-cell-type expression variation to resolve spatially distinct populations while taking into account prior knowledge, as well as probe design and expression constraints. To facilitate data analysis and interpretation, Spapros also provides rules for cell type identification. We evaluated Spapros by selecting probes on 6 different data sets and built an evaluation pipeline with 12 quality metrics to find that Spapros outperforms other selection approaches in both cell type recovery and recovering expression variation beyond cell types. Furthermore, we used Spapros to design a SCRINSHOT experiment of adult lung tissue to demonstrate how probes selected with Spapros identify cell types of interest and detect spatial variation even within cell types. Spapros enables optimal probe set selection, probe set evaluation, and probe design, as a freely available Python package.

bioinformatics↗

bioRxiv2020

Regression plane concept: analysing continuous cellular processes with machine learning

Biological processes are inherently continuous, and the chance of phenotypic discovery is significantly restricted by discretising them. Using multi-parametric active regression we introduce a novel concept to describe and explore biological data in a continuous manner. We have implemented Regression Plane (RP), the first user-friendly discovery tool enabling class-free phenotypic supervised machine learning.

bioinformatics↗

bioRxiv2020

Automatic deep learning driven label-free image guided patch clamp system for human and rodent in vitro slice physiology

Patch clamp recording of neurons is a labor-intensive and time-consuming procedure. We have developed a tool that fully automatically performs electrophysiological recordings in label-free tissue slices. The automation covers the detection of cells in label-free images, calibration of the micropipette movement, approach to the cell with the pipette, formation of the whole-cell configuration, and recording. The cell detection is based on deep learning. The model was trained on a new image database of neurons in unlabeled brain tissue slices. The pipette tip detection and approaching phase use image analysis techniques for precise movements. High-quality measurements were performed on hundreds of human and rodent neurons. We also demonstrate that further molecular and anatomical analysis can be performed on the recorded cells. The software has a diary module that automatically logs patch clamp events. Our tool can multiply the number of daily measurements to help brain research. ONE SENTENCE SUMMARYNovel deep learning and image analysis algorithms for automated patch clamp systems to reliably measure neurons in human and rodent brain slices.

neuroscience↗

bioRxiv2019

A deep learning framework for nucleus segmentation using image style transfer

Single cell segmentation is typically one of the first and most crucial tasks of image-based cellular analysis. We present a deep learning approach aiming towards a truly general method for localizing nuclei across a diverse range of assays and light microscopy modalities. We outperform the 739 methods submitted to the 2018 Data Science Bowl on images representing a variety of realistic conditions, some of which were not represented in the training data. The key to our approach is to adapt our model to unseen and unlabeled data using image style transfer to generate augmented training samples. This allows the model to recognize nuclei in new and different experiments without requiring expert annotations.

bioinformatics↗

bioRxiv2017

A deep convolutional neural network approach for astrocyte detection

Astrocytes are involved in brain pathologies such as trauma or stroke, neurodegenerative disorders like Alzheimers and Parkinsons disease, chronic pain, and many others. Determining cell density and timing of morphological and biochemical changes is important for a proper understanding of the role of astrocytes in physiological and pathological conditions. One of the most important of such analyses is astrocytes count within a complex tissue environment in microscopy images. The most widely used approaches for the quantification of microscopy images data are either manual stereological cell counting or semi-automatic segmentation techniques. Detecting astrocytes automatically is a highly challenging computational task, for which we currently lack efficient image analysis tools. In this study, we developed a fast and fully automated software that assesses the number of astrocytes using Deep Convolutional Neural Networks (DCNN). The method highly outperforms state-of-the-art image analysis and machine learning methods and provides detection accuracy and precision comparable to that of human experts. Additionally, the runtime of cell detection is significantly less than other three analyzed computational methods, and it is faster than human observers by orders of magnitude. We applied DCNN-based method to examine the number of astrocytes in different brain regions of rats with opioid-induced hyperalgesia/tolerance (OIH/OIT) as morphine tolerance is believed to activate glial cells in the brain. We observed strong positive correlation between manual cell detection and DCNN-based analysis method for counting astrocytes in the brains of experimental animals.

bioinformatics↗

bioRxiv2017

Show me your neighbours, and I’ll tell you what you are – cellular microenvironment matters

To answer major questions of cell biology, it is essential to understand cellular complexity. Modern automated microscopes produce vast amounts of images routinely, making manual analysis nearly impossible. Due to their efficiency, machine learning-based analysis software have become essential tools to perform single-cell-level phenotypic analysis of large imaging datasets. However, an important limitation of such methods is that they do not use the information gained from the cellular micro- and macroenvironment: the algorithmic decision is based solely on the local properties of the cell of interest. Here, we present how various microenvironmental features contribute to identifying a cell and how such additional information can improve single-cell-level phenotypic image analysis. The proposed methodology was tested for different sizes of Euclidean and nearest neighbour-based cellular environments both on tissue sections and cell cultures. Our experimental data verify that the microenvironment of a cell largely determines its entity. This effect was found to be especially strong for established tissues, while it was somewhat weaker in the case of cell cultures. Our analysis shows that combining local cellular features with the properties of the cell's microenvironment significantly improves the accuracy of machine learning-based phenotyping.

bioinformatics↗