Group leader: Péter HORVÁTH

Email: horvath.peter[at]brc.hu

Group website:

http://sysbiol.brc.hu/horvath-peter-lab-index.html

Group members

Name

Title

 

 

Péter HORVÁTH

Senior Research Associate

publications

CV

Krisztina BUZÁS

Research Associate

publications

 

Krisztián KOÓS

Research Associate

publications

 

Ede MIGH

Research Associate

publications

 

József MOLNÁR

Research Associate

publications

 

Edina GYUKITY-SEBESTYÉN

Junior Research Associate

publications

 

Mária HARMATI

Junior Research Associate

publications

 

Dávid BAUER

Scientific Administrator

 

 

Attila BELEON

Scientific Administrator

 

 

Gabriella GRESKOVICS-DOBRA

Scientific Administrator

publications

 

Réka HOLLANDI

Scientific Administrator

publications

 

Ákos DIÓSDI

PhD Student

publications

 

István GREXA

PhD Student

publications

 

Dominik HIRLING

PhD Student

publications

 

Nikita MOSHKOV

PhD Student

publications

 

Ervin TASNÁDI

PhD Student

publications

 

Tímea TÓTH

PhD Student

publications

 

Tímea BÖRÖCZKY

Undergraduate Student

 

 

Filippo PICCININI

Research Associate

publications

 

András KRISTON

Scientific Administrator

publications

Research

Overview

Recent technological advancements in systems biology, lab automation, and high-throughput microscopy have opened the door to systematic discovery of complex biological systems using high-throughput light microscopy. Modern equipments produce massive amounts of data which cannot be analyzed manually.

Automating the analysis process poses several challenges related to computational cell biology. Our group dedicated to finding computational solutions to biological problems. Our research focuses on the intersection of biology and computer science, and combine wet-lab and light microscopy with image analysis and machine learning methods.


   Image analysis           Machine learning    Single cell analysis            Imaging

Imaging

 


I. Image analysis

Illumination Correction
For quantitative measurements based on light microscopy and especially fluorescent intensities, it is essential to normalize the image data to correct for aberrations inherent in the acquisition process. One common source of error is the result of a non-ideal illumination field produced by the objective. Our novel algorithms addresses these issues using energy minimization. The corrected field resulting from our approach is extremely flat, and we can achieve this level of quality without requiring a calibrated reference sample.


Reconstruction
We developed an algorithm based on energy minimization to convert differential interference contrast (DIC) images to phase images to make them easier to analyze.


Tracking
We are interested in developing methods for identifying and tracking cells or sub-cellular structures on live cell images. We have been developed a software the CellTracker, which corrects illumination problems, finds alignments, as well as automatically and manually tracks cells, mainly on phase contrast images. The program is available with MATLAB GUI.
[Download CellTracker]


Segmentation of overlapping cells (the 'gas of circles' model)
Variational methods for shape modeling to extract near-circular objects (e.g. nuclei).
The multi-layered 'gas of near-circles' model is capable of segmenting touching or even overlapping cells on high confluency images.


Selective Active Contours
The selective active contours utilize simple shape characteristics such as area and perimeter, to describe objects that can provide computationally efficient shape selective segmentation.


Selective Active Contours in 3D
3D extension of the selective 2D active contours.

  • Computationally expensive
  • Utilizing GPU-s to achieve high enough performance for practical use

                            

 Cells in pseudohyphae and in normal form.                   Finding the pseudohyphae form.

 

 


Spliting touching cells
Segment individual cell nuclei by splitting touching ones. The two-step approach merely based on energy minimization principles using a higher-order active contour framework.

 



II. Machine learning

Phenotyping - Advanced Cell Classifier
Advanced Cell Classifier is a data analyzer program to evaluate cell-based high-content screens and tissue section images developed at the Biological Research Centre, Szeged and FIMM, Helsinki (formerly at ETH Zurich). The basic aim is to provide a very accurate analysis with minimal user interaction using advanced machine learning methods. ACC was used to analyze some of the first large whole genome scale RNAi screens and all together for more than 300.000.000 images and several billion single cell-based machine learning decisions.

  • most accurate analysis
  • minimal user interaction
  • intelligent modules
  • performance feedback
  • advanced machine learning


Deep Learning
We developed a fast and fully automated tools that assesses the number and location of cells using Deep Convolutional Neural Networks (DCNN). Our methods highly outperforms state-of-the-art machine learning models and provides comparable detection accuracy to human field experts.

        

 


Microenvironment-based phenotyping
We research how various microenvironmental features contribute to identifying a cell and how such additional information can improve single-cell-level phenotypic image analysis.



 

III. Single Cell Analysis Methods

CAMI - Computer Aided Microscopy Isolation system
We develop a high-throughput, non-disruptive, and cost-effective isolation methods that is capable of capturing individually targeted cells using widely available techniques. Using high-resolution microscopy, laser microcapture microscopy, image analysis, and machine learning, our technology enables scalable molecular genetic analysis of single cells, targetable by morphology or location within the sample. Cell data along with the location and contour ofeach cell is sent to our interactive online database CAMIO.


AutoPatcher
We are building an automated patch clamp system to analyze the electrophysiological properties of neurons in vitro. The system automatically selects a cell in label-free microscopy and performs patch clamping on it using image processing and deep learning.

                 



Microscopy

High content screening
The HCS technology employs different automated microscopes in a high throughput format to extract quantitative information from cells or tissue samples based on various parameters such as spatial distribution or the morphology changes of the target cells. To address these various cellular phenotypes, both widefield and confocal microscopes are used in the BIOMAG group:

PerkinElmer Operetta
Main features:

  • laser-based autofocus system
  • objectives ranging from low to high magnifications(2-100X)
  • objectives with high numerical aperture
  • live cell imaging


Analysis of cellular phenotypes:

  • apoptosis and cell cycle studies
  • cell differentiation and cell migration assays
  • cell proliferation and cell shape changes
  • cytoskeletal rearrangement and cytotoxicity
  • protein expression and translocation experiments
  • wound healing assays


Leica SP8-digital light sheet

  • DMi8 series with a motorized objective revolver
  • automated image acquisition for mosaic or multiwell applications
  • super-sensitive photon detection making it ideal for low light and live cell imaging
  • the platform can be turned into a light sheet microscope


 

 

Laser microdissection systems
During LDM, a laser is focused on the tissue and it cuts the sample alongside a predefined trajectory. After the cutting process, the required elements can be extracted and collected for further analysis. The dissected material is then available for further downstream applications such as genomics, transcriptomics, next generation sequencing, proteomics or other analytical techniques. Based on the movement of the laser and sample collection, two main approaches have been used:


Leica LMD6

  • the movement of the laser beam is achieved via optics
  • the specimen is collected via gravity
  • fully automated upright research microscope

 

 

 


Zeiss Palm Microbeam

  • motorized microscope stage
  • laser catapulting
  • standard microscopic slides can also be used