Correlative Fluorescence and Raman Microscopy to Define Mitotic Stages at the Single-Cell Level: Opportunities and Limitations in the AI Era

Nowadays, morphology and molecular analyses at the single-cell level have a fundamental role in understanding biology better. These methods are utilized for cell phenotyping and in-depth studies of cellular processes, such as mitosis. Fluorescence microscopy and optical spectroscopy techniques, including Raman micro-spectroscopy, allow researchers to examine biological samples at the single-cell level in a non-destructive manner. Fluorescence microscopy can give detailed morphological information about the localization of stained molecules, while Raman microscopy can produce label-free images at the subcellular level; thus, it can reveal the spatial distribution of molecular fingerprints, even in live samples. Accordingly, the combination of correlative fluorescence and Raman microscopy (CFRM) offers a unique approach for studying cellular stages at the singlecell level. However, subcellular spectral maps are complex and challenging to interpret. Artificial intelligence (AI) may serve as a valuable solution to characterize the molecular backgrounds of phenotypes and biological processes by finding the characteristic patterns in spectral maps. The major contributions of the manuscript are: (I) it gives a comprehensive review of the literature focusing on AI techniques in Raman-based cellular phenotyping; (II) via the presentation of a case study, a new neural network-based approach is described, and the opportunities and limitations of AI, specifically deep learning, are discussed regarding the analysis of Raman spectroscopy data to classify mitotic cellular stages based on their spectral maps

Local cellular neighbourhood controls proliferation in cell competition

Cell competition is a quality control mechanism through which tissues eliminate unfit cells. Cell competition can result from short-range biochemical inductions or long-range mechanical cues. However, little is known about how cell-scale interactions give rise to population shifts in tissues, due to the lack of experimental and computational tools to efficiently characterise interactions at the single-cell level. Here, we address these challenges by combining long-term automated microscopy with deep learning image analysis to decipher how single-cell behaviour determines tissue make-up during competition. Using our high-throughput analysis pipeline, we show that competitive interactions between MDCK wild-type cells and cells depleted of the polarity protein scribble are governed by differential sensitivity to local density and the cell-type of each cell's neighbours. We find that local density has a dramatic effect on the rate of division and apoptosis under competitive conditions. Strikingly, our analysis reveals that proliferation of the winner cells is upregulated in neighbourhoods mostly populated by loser cells. These data suggest that tissue-scale population shifts are strongly affected by cellular-scale tissue organisation. We present a quantitative mathematical model that demonstrates the effect of neighbour cell-type dependence of apoptosis and division in determining the fitness of competing cell lines

Modeling meiotic recombination hotspots using deep learning

La recombinaison méiotique joue un rôle essentiel dans la ségrégation des chromosomes pendant la méiose et dans la création de nouvelles combinaisons du matériel génétique des espèces. Ses effets cause une déviation du principe de l'assortiment indépendant de Mendel; cependant, les mécanismes moléculaires impliqués restent partiellement incompris jusqu'à aujourd'hui. Il s'agit d'un processus hautement régulé et de nombreuses protéines sont impliquées dans son contrôle, dirigeant la recombinaison méiotique dans des régions génomiques de 1 à 2 kilobases appelées « hotspots ». Au cours des dernières années, l'apprentissage profond a été appliqué avec succès à la classification des séquences génomiques. Dans ce travail, nous appliquons l'apprentissage profond aux séquences d'ADN humain afin de prédire si une région spécifique d'ADN est un hotspot de recombinaison méiotique ou non. Nous avons appliqué des réseaux de neurones convolutifs sur un ensemble de données décrivant les hotspots de quatre individus non-apparentés, atteignant une exactitude de plus de 88 % avec une précision et un rappel supérieur à 90 % pour les meilleurs modèles. Nous explorons l'impact de différentes tailles de séquences d'entrée, les stratégies de séparation des jeux d'entraînement/validation et l’utilité de montrer au modèle les coordonnées génomiques de la séquence d'entrée. Nous avons exploré différentes manières de construire les motifs appris par le réseau et comment ils peuvent être liés aux méthodes classiques de construction de matrices position-poids, et nous avons pu déduire des connaissances biologiques pertinentes découvertes par le réseau. Nous avons également développé un outil pour visualiser les différents modèles afin d'aider à interpréter les différents aspects du modèle. Dans l'ensemble, nos travaux montrent la capacité des méthodes d'apprentissage profond à étudier la recombinaison méiotique à partir de données génomiques.Meiotic recombination plays a critical role in the proper segregation of chromosomes during meiosis and in forming new combinations of genetic material within sexually-reproducing species. For a long time, its side effects were observed as a deviation from the Mendel’s principle of independent assortment; however, its molecular mechanisms remain only partially understood until today. We know that it is a highly regulated process and that many molecules are involved in this tight control, resulting in directing meiotic recombination into 1-2 kilobase genomic pairs regions called hotspots. During the past few years, deep learning was successfully applied to the classification of genomic sequences. In this work, we apply deep learning to DNA sequences in order to predict if a specific stretch of DNA is a meiotic recombination hotspot or not. We applied convolution neural networks on a dataset describing the hotspots of four unrelated male individuals, achieving an accuracy of over 88% with precision and recall above 90% for the best models. We explored the impact of different input sequence lengths, train/validation split strategies and showing the model the genomic coordinates of the input sequence. We explored different ways to construct the learnt motifs by the network and how they can relate to the classical methods of constructing position-weight-matrices, and we were able to infer relevant biological knowledge uncovered by the network. We also developed a tool for visualizing the different models output in order to help digest the different aspects of the model. Overall, our work shows the ability for deep learning methods to study meiotic recombination from genomic data

Investigating the use of Deep Learning Algorithms to Automatically Score Micronuclei in Human Cell Lines

The in vitro micronucleus (MN) assay is a globally used test to quantify DNA damage induced by test chemicals from various industries such as pharmaceuticals, cosmetics and agriculture. Currently, manual scoring is used which is extremely time-consuming and scorer subjective so causes a significant bottleneck in the use of the MN assay. This project shows that imaging flow cytometry coupled with deep learning neural networks can be reliably and accurately used with inter-laboratory function, to automatically score micronucleus events in chemically exposed human B lymphoblastoid cells called TK6 cells. Images were taken from both the cytokinesis-block micronucleus (CBMN) assay and the mononucleate MN assay at Newcastle University. Six different chemicals were tested in this study which are known genotoxic agents and known non-genotoxic agents: aroclor, carbendazim, methyl methanosulphate (MMS), vinblastine, benzo(a)pyrene, D-mannitol. These images were then inputted into a “Deep Flow” neural network, coded in the MATLAB platform which was previously trained on human-scored images assembled from the CBMN assay conducted by Cardiff and Cambridge universities, using MMS and carbendazim treated TK6 cells. Using image data from multiple laboratories in this study provides evidence that the neural network can be used to score unseen data from any laboratory. The neural network correctly scores micronucleus events for both the CBMN and mononucleate MN assays at a percentage confidence of 70% and above. Dose response data for each chemical is parallel to ECVAM guidelines. The aneugen, carbendazim, was shown by the deep learning algorithm to increase the mean dose response by 3.4-fold which shows that as the dose of carbendazim increases, the abundance of micronuclei increases. Further optimisation of the ground truth will prevent underscoring of micronuclei in binucleated cells. It can be concluded that with further optimisation and development of the neural network, this automated platform offers a great potential for the use of the in vitro MN assay to be widened. This method has a higher throughput and has the capability to test greater numbers of compounds and chemicals, therefore, this method will be able to keep up with the increasing demand for genotoxicity testing in industrial and pharmaceutical settings