Helmholtz AI consultants @ HMGU

Identification of infected cells from unlabelled microscopy images

• Challenge: Augmented microscopy techniques can employ the power of deep neural networks to predict fluorescent labels from transmitted light images. These networks need to be trained on large datasets of fluorescent images, but once trained are able to predict various cellular structures, such as the cell nucleus or membrane, from images of unstained cells. So far, no method exists which can reconstruct fluorescent labels of infection markers and scientists are not even certain if this is possible, since infected cells in brightfield images are not distinguishable from healthy ones.
• Approach: We are currently working on a proof of concept that neural networks are able to reconstruct infection labels from brightfield microscopy images. We base our work on state-of-the-art research that demonstrates the capability of CNNs to reconstruct other fluorescent markers, such as the DAPI marker that highlights the cell nucleus. Our method implements a  U-Net and requires a single 2D brightfield images as input. We attempt to learn infection channels by having the network focus on image regions that are heavily infected. By applying different machine learning methods  and visualization techniques we are able to see if our deep learning model can indeed identify cell infection.

Enzyme Function Prediction

• Challenge: Enzymes are an subset of proteins which are important biochemical catalysts regulating many biological functions, such as aiding in chemical transport or cleaving molecules. The enzyme function is determined by its chemical function, which is described by a local chemical environment: the enzyme binding site. In rational drug design this binding site information is crucial. With it new structures can be predicted to activate or inhibit the function of biomolecules. Although there are many methods to classify enzymes using sequence information, there are few methods that take into account 3D structure and atom positions. Another challenge is that enzyme functions are organized as a hierarchical tree structure, which require a method to classify enzyme function well on different levels.
• Approach: We have implemented classical 3D Convolutional neural networks with rotation augmentation. As a more advanced approach, we also implemented methods which are rotation invariant and uses distance and/or angle information between atoms of the enzyme. We will also implement hierarchical classification, and an Imbalanced Data sampler which over samples minority classes, as the dataset is highly imbalanced.

Wavelet-based Event Separation

• Challenge: Researchers at HMGU are interested to find new ways to analyse high resolution spatio-temporal data of of brain activity. Dependent on experimental conditions brain-activity can range from seemingly chaotic dynamics to slow traveling wave phenomena. The goal of this project was to disentangle the complex dynamics and isolate phenomena in the brain in order to better understand the dynamics in the different experimental conditions. One of the challenges hereby is that the timescales of the phenomena in the chaotic regime are unknown.
• Approach: We used a continuous wavelet analysis coupled to a cluster analysis, to divide the complex multi scale time series into several modes and separate the relevant phenomena in the data.

Automatic Cell Counting in cell migration experiments

• Challenge: Cell migration is central to many physiological and pathological processes such as embryonic development, wound repair, and tumor metastasis. Boyden Chamber assay is the most widely accepted cell migration technique for the characterization of cell motility. Cell motility is quantified by counting the cell numbers in the microscopic images. Such images normally contain many cells and therefore counting manually is quite time consuming, laborious and error prone.
• Approach: An automatic cell counter algorithm is provided to count crystal violet cell numbers in 2D microscopic images. In addition, a graphical user interface is also implemented for further manual correction of the automatic results. Our solution permits to speed up the analysis in cell migration experiments by a factor of 10.

CRISPRi guide efficiency prediction in bacteria

• Challenge: CRISPR interference (CRISPRi) has become a prevalent technique in bacteria for studying the function of individual genes, regulating pathways for metabolic engineering, and performing genome-wide genetic screens. However, design tools for guide selection remain to be developed for CRISPRi despite their availability and common use for other CRISPR technologies. The goal of this project was the development of a model that accurately predicts guide depletion in publicly available CRISPRi essentiality screens in Escherichia coli, using a variety of sequence and thermodynamic features.
• Approach: The efficiency of guide RNAs can be measured with genome-wide essentiallity screens. However, the efficiency calculated from these screens can only serve as a proxy for guide efficiency because it contains confounding gene effects. To correct the guide efficiency for those gene effects, we used a median subtraction approach. In a second step, we used the corrected guide efficiency to develop a model that can predict the guide efficiency of unseen guides from different sequence and thermodynamic features. Here, we compared a 1D convolutional neural network with a recurrent neural network to investigate if the sequential information in the guide RNA sequence is more informative than the positional information, i.e. which nucleotide can be found at a certain position in the guide sequence, and conclude that the positional information is more informative than the sequential information. Furthermore, the trained 1D CNN model accurately predicts efficiency of 750 guides specific to nine purine genes essential in minimal media in Escherichia coli.

PySDDR

PySDDR combines the interpretability of a statistical model with the prediction power of deep neural networks in an easy-to-use python package. It is the python implementation of the Semi-Structured Deep Distributional Regression (SDDR) framework  which enhances Generalized Additive Models (GAMs) with neural networks. This extends the use of GAMs to model high-dimensional nonlinear patterns in the data and, furthermore, to be applied to multimodal data (e.g. a combination of tabular and image data). The framework is written in PyTorch and accepts any number of neural networks, of any type (FC, CNN, LSTM, ...).

https://github.com/HelmholtzAI-Consultants-Munich/PySDDR

MALIS loss

The MALIS (Maximin Affinity Learning of Image Segmentation) loss is a structured loss function for supervised learning of segmentation, that predicts the weights of the nearest neighbor pixel affinity graph. MALIS loss can outperform the widely used cross-entropy or the Dice-coefficient loss functions in instance segmentation tasks, especially for separating closeby or touching instances. This is the first implementation of the loss in Pytorch, and it can also be used with Tensorflow.

https://github.com/HelmholtzAI-Consultants-Munich/Malis-Loss

Automatic Cell Counter

This automatic cell counter algorithm permits to count crystal violet cells in 2D microscopic images of Boyden Chamber assay. This simple pipeline, based on classical computer vision algorithms, permits to distinguish cells from the chamber pores, who have a similar color spectrum. An easy-to-use graphical user interface is proposed if further manual correction of automatic results is needed. This software is speeding up the analysis of cell migration experiments by a factor 10.

https://github.com/HelmholtzAI-Consultants-Munich/Automatic-Cell-Counter