Deep Learning Methods for Detection and Tracking of Particles in Fluorescence Microscopy Images

Studying the dynamics of sub-cellular structures such as receptors, filaments, and vesicles is a prerequisite for investigating cellular processes at the molecular level. In addition, it is important to characterize the dynamic behavior of virus structures to gain a better understanding of infection mechanisms and to develop novel drugs. To investigate the dynamics of fluorescently labeled sub-cellular and viral structures, time-lapse fluorescence microscopy is the most often used imaging technique. Due to the limited spatial resolution of microscopes caused by diffraction, these very small structures appear as bright, blurred spots, denoted as particles, in microscopy images. To draw statistically meaningful biological conclusions, a large number of such particles need to be analyzed. However, since manual analysis of fluorescent particles is very time consuming, fully automated computer-based methods are indispensable. We introduce novel deep learning methods for detection and tracking of multiple particles in fluorescence microscopy images. We propose a particle detection method based on a convolutional neural network which performs image-to-image mapping by density map regression and uses the adaptive wing loss. For particle tracking, we present a recurrent neural network that exploits past and future information in both forward and backward direction. Assignment probabilities across multiple detections as well as the probabilities for missing detections are computed jointly. To resolve tracking ambiguities using future information, several track hypotheses are propagated to later time points. In addition, we developed a novel probabilistic deep learning method for particle tracking, which is based on a recurrent neural network mimicking classical Bayesian filtering. The method includes both aleatoric and epistemic uncertainty, and provides valuable information about the reliability of the computed trajectories. Short and long-term temporal dependencies of individual object dynamics are exploited for state prediction, and assigned detections are used to update the predicted states. Moreover, we developed a convolutional Long Short-Term Memory neural network for combined particle tracking and colocalization analysis in two-channel microscopy image sequences. The network determines colocalization probabilities, and colocalization information is exploited to improve tracking. Short and long-term temporal dependencies of object motion as well as image intensities are taken into account to compute assignment probabilities jointly across multiple detections. We also introduce a deep learning method for probabilistic particle detection and tracking. For particle detection, temporal information is integrated to regress a density map and determine sub-pixel particle positions. For tracking, a fully Bayesian neural network is presented that mimics classical Bayesian filtering and takes into account both aleatoric and epistemic uncertainty. Uncertainty information of individual particle detections is considered. Network training for the developed deep learning-based particle tracking methods relies only on synthetic data, avoiding the need of time-consuming manual annotation. We performed an extensive evaluation of our methods based on image data of the Particle Tracking Challenge as well as on fluorescence microscopy images displaying virus proteins of HCV and HIV, chromatin structures, and cell-surface receptors. It turned out that the methods outperform previous methods

PITX1 is a regulator of TERT expression in prostate cancer with prognostic power

Simple Summary Most prostate cancer is of an indolent form and is curable. However, some prostate cancer belongs to rather aggressive subtypes leading to metastasis and death, and immediate therapy is mandatory. However, for these, the therapeutic options are highly invasive, such as radical prostatectomy, radiation or brachytherapy. Hence, a precise diagnosis of these tumor subtypes is needed, and the thus far applied diagnostic means are insufficient for this. Besides this, for their endless cell divisions, prostate cancer cells need the enzyme telomerase to elongate their telomeres (chromatin endings). In this study, we developed a gene regulatory model based on large data from transcription profiles from prostate cancer and chromatin-immuno-precipitation studies. We identified the developmental regulator PITX1 regulating telomerase. Besides observing experimental evidence of PITX1′s functional role in telomerase regulation, we also found PITX1 serving as a prognostic marker, as concluded from an analysis of more than 15,000 prostate cancer samples. Abstract The current risk stratification in prostate cancer (PCa) is frequently insufficient to adequately predict disease development and outcome. One hallmark of cancer is telomere maintenance. For telomere maintenance, PCa cells exclusively employ telomerase, making it essential for this cancer entity. However, TERT, the catalytic protein component of the reverse transcriptase telomerase, itself does not suit as a prognostic marker for prostate cancer as it is rather low expressed. We investigated if, instead of TERT , transcription factors regulating TERT may suit as prognostic markers. To identify transcription factors regulating TERT , we developed and applied a new gene regulatory modeling strategy to a comprehensive transcriptome dataset of 445 primary PCa. Six transcription factors were predicted as TERT regulators, and most prominently, the developmental morphogenic factor PITX1. PITX1 expression positively correlated with telomere staining intensity in PCa tumor samples. Functional assays and chromatin immune-precipitation showed that PITX1 activates TERT expression in PCa cells. Clinically, we observed that PITX1 is an excellent prognostic marker, as concluded from an analysis of more than 15,000 PCa samples. PITX1 expression in tumor samples associated with (i) increased Ki67 expression indicating increased tumor growth, (ii) a worse prognosis, and (iii) correlated with telomere length

Deep probabilistic tracking of particles in fluorescence microscopy images

Tracking of particles in temporal fluorescence microscopy image sequences is of fundamental importance to quantify dynamic processes of intracellular structures as well as virus structures. We introduce a probabilistic deep learning approach for fluorescent particle tracking, which is based on a recurrent neural network that mimics classical Bayesian filtering. Compared to previous deep learning methods for particle tracking, our approach takes into account uncertainty, both aleatoric and epistemic uncertainty. Thus, information about the reliability of the computed trajectories is determined. Manual tuning of tracking parameters is not necessary and prior knowledge about the noise statistics is not required. Short and long-term temporal dependencies of individual object dynamics are exploited for state prediction, and assigned detections are used to update the predicted states. For correspondence finding, we introduce a neural network which computes assignment probabilities jointly across multiple detections as well as determines the probabilities of missing detections. Training requires only simulated data and therefore tedious manual annotation of ground truth is not needed. We performed a quantitative performance evaluation based on synthetic and real 2D as well as 3D fluorescence microscopy images. We used image data of the Particle Tracking Challenge as well as real time-lapse fluorescence microscopy images displaying virus structures and chromatin structures. It turned out that our approach yields state-of-the-art results or improves the tracking results compared to previous methods

APPsα rescues Tau-induced synaptic pathology

Alzheimer's disease (AD) is histopathologically characterized by Aβ plaques and the accumulation of hyperphosphorylated Tau species, the latter also constituting key hallmarks of primary tauopathies. Whereas Aβ is produced by amyloidogenic APP processing, APP processing along the competing non-amyloidogenic pathway results in the secretion of neurotrophic and synaptotrophic APPsα. Recently, we demonstrated that APPsα has therapeutic effects in transgenic AD model mice and rescues Aβ-dependent impairments. Here, we examined the potential of APPsα to mitigate Tau-induced synaptic deficits in P301S mice (both sexes), a widely used mouse model of tauopathy. Analysis of synaptic plasticity revealed an aberrantly increased LTP in P301S mice that could be normalized by acute application of nanomolar amounts of APPsα to hippocampal slices, indicating a homeostatic function of APPsα on a rapid time scale. Further, AAV-mediated in vivo expression of APPsα restored normal spine density of CA1 neurons even at stages of advanced Tau pathology not only in P301S mice, but also in independent THY-Tau22 mice. Strikingly, when searching for the mechanism underlying aberrantly increased LTP in P301S mice, we identified an early and progressive loss of major GABAergic interneuron subtypes in the hippocampus of P301S mice, which may lead to reduced GABAergic inhibition of principal cells. Interneuron loss was paralleled by deficits in nest building, an innate behavior highly sensitive to hippocampal impairments. Together, our findings indicate that APPsα has therapeutic potential for Tau-mediated synaptic dysfunction and suggest that loss of interneurons leads to disturbed neuronal circuits that compromise synaptic plasticity as well as behavior.SIGNIFICANCE STATEMENTOur findings indicate for the first time that APPsα has the potential to rescue Tau-induced spine loss and abnormal synaptic plasticity. Thus, APPsα might have therapeutic potential not only due to its synaptotrophic functions, but also its homeostatic capacity for neuronal network activity. Hence, APPsα is one of the few molecules which has proven therapeutic effects in mice, both for Aβ- and Tau-dependent synaptic impairments and might therefore have therapeutic potential for patients suffering from AD or primary tauopathies. Furthermore, we found in P301S mice a pronounced reduction of inhibitory interneurons as the earliest pathological event preceding the accumulation of hyperphosphorylated Tau species. This loss of interneurons most likely disturbs neuronal circuits that are important for synaptic plasticity and behavior