Computational Image Analysis For Axonal Transport, Phenotypic Profiling, And Digital Pathology

Recent advances in fluorescent probes, microscopy, and imaging platforms have revolutionized biology and medicine, generating multi-dimensional image datasets at unprecedented scales. Traditional, low-throughput methods of image analysis are inadequate to handle the increased “volume, velocity, and variety” that characterize the realm of big data. Thus, biomedical imaging requires a new set of tools, which include advanced computer vision and machine learning algorithms. In this work, we develop computational image analysis solutions to biological questions at the level of single-molecules, cells, and tissues. At the molecular level, we dissect the regulation of dynein-dynactin transport initiation using in vitro reconstitution, single-particle tracking, super-resolution microscopy, live-cell imaging in neurons, and computational modeling. We show that at least two mechanisms regulate dynein transport initiation neurons: (1) cytoplasmic linker proteins, which are regulated by phosphorylation, increase the capture radius around the microtubule, thus reducing the time cargo spends in a diffusive search; and (2) a spatial gradient of tyrosinated alpha-tubulin enriched in the distal axon increases the affinity of dynein-dynactin for microtubules. Together, these mechanisms support a multi-modal recruitment model where interacting layers of regulation provide efficient, robust, and spatiotemporal control of transport initiation. At the cellular level, we develop and train deep residual convolutional neural networks on a large and diverse set of cellular microscopy images. Then, we apply networks trained for one task as deep feature extractors for unsupervised phenotypic profiling in a different task. We show that neural networks trained on one dataset encode robust image phenotypes that are sufficient to cluster subcellular structures by type and separate drug compounds by the mechanism of action, without additional training, supporting the strength and flexibility of this approach. Future applications include phenotypic profiling in image-based screens, where clustering genetic or drug treatments by image phenotypes may reveal novel relationships among genetic or pharmacologic pathways. Finally, at the tissue level, we apply deep learning pipelines in digital pathology to segment cardiac tissue and classify clinical heart failure using whole-slide images of cardiac histopathology. Together, these results demonstrate the power and promise of computational image analysis, computer vision, and deep learning in biological image analysis

Oncogenic Kras Activates a Hematopoietic-to-Epithelial IL-17 Signaling Axis in Preinvasive Pancreatic Neoplasia

SummaryMany human cancers are dramatically accelerated by chronic inflammation. However, the specific cellular and molecular elements mediating this effect remain largely unknown. Using a murine model of pancreatic intraepithelial neoplasia (PanIN), we found that KrasG12D induces expression of functional IL-17 receptors on PanIN epithelial cells and also stimulates infiltration of the pancreatic stroma by IL-17-producing immune cells. Both effects are augmented by associated chronic pancreatitis, resulting in functional in vivo changes in PanIN epithelial gene expression. Forced IL-17 overexpression dramatically accelerates PanIN initiation and progression, while inhibition of IL-17 signaling using genetic or pharmacologic techniques effectively prevents PanIN formation. Together, these studies suggest that a hematopoietic-to-epithelial IL-17 signaling axis is a potent and requisite driver of PanIN formation

LC3 Binding to the Scaffolding Protein JIP1 Regulates Processive Dynein-Driven Transport of Autophagosomes

Autophagy is essential for maintaining cellular homeostasis in neurons, where autophagosomes undergo robust unidirectional retrograde transport along axons. We find that the motor scaffolding protein JIP1 binds directly to the autophagosome adaptor LC3 via a conserved LIR motif. This interaction is required for the initial exit of autophagosomes from the distal axon, for sustained retrograde transport along the midaxon, and for autophagosomal maturation in the proximal axon. JIP1 binds directly to the dynein activator dynactin but also binds to and activates kinesin-1 in a phosphorylation-dependent manner. Following JIP1 depletion, phosphodeficient JIP1-S421A rescues retrograde transport, while phosphomimetic JIP1-S421D aberrantly activates anterograde transport. During normal autophagosome transport, residue S421 of JIP1 may be maintained in a dephosphorylated state by autophagosome-associated MKP1 phosphatase. Moreover, binding of LC3 to JIP1 competitively disrupts JIP1-mediated activation of kinesin. Thus, dual mechanisms prevent aberrant activation of kinesin to ensure robust retrograde transport of autophagosomes along the axon

α-Tubulin Tyrosination and CLIP-170 Phosphorylation Regulate the Initiation of Dynein-Driven Transport in Neurons

Motor-cargo recruitment to microtubules is often the rate-limiting step of intracellular transport, and defects in this recruitment can cause neurodegenerative disease. Here, we use in vitro reconstitution assays with single-molecule resolution, live-cell transport assays in primary neurons, computational image analysis, and computer simulations to investigate the factors regulating retrograde transport initiation in the distal axon. We find that phosphorylation of the cytoskeletal-organelle linker protein CLIP-170 and post-translational modifications of the microtubule track combine to precisely control the initiation of retrograde transport. Computer simulations of organelle dynamics in the distal axon indicate that while CLIP-170 primarily regulates the time to microtubule encounter, the tyrosination state of the microtubule lattice regulates the likelihood of binding. These mechanisms interact to control transport initiation in the axon in a manner sensitive to the specialized cytoskeletal architecture of the neuron

Orientation-invariant autoencoders learn robust representations for shape profiling of cells and organelles

Abstract Cell and organelle shape are driven by diverse genetic and environmental factors and thus accurate quantification of cellular morphology is essential to experimental cell biology. Autoencoders are a popular tool for unsupervised biological image analysis because they learn a low-dimensional representation that maps images to feature vectors to generate a semantically meaningful embedding space of morphological variation. The learned feature vectors can also be used for clustering, dimensionality reduction, outlier detection, and supervised learning problems. Shape properties do not change with orientation, and thus we argue that representation learning methods should encode this orientation invariance. We show that conventional autoencoders are sensitive to orientation, which can lead to suboptimal performance on downstream tasks. To address this, we develop O2-variational autoencoder (O2-VAE), an unsupervised method that learns robust, orientation-invariant representations. We use O2-VAE to discover morphology subgroups in segmented cells and mitochondria, detect outlier cells, and rapidly characterise cellular shape and texture in large datasets, including in a newly generated synthetic benchmark

A deep-learning classifier identifies patients with clinical heart failure using whole-slide images of H&E tissue

<div><p>Over 26 million people worldwide suffer from heart failure annually. When the cause of heart failure cannot be identified, endomyocardial biopsy (EMB) represents the gold-standard for the evaluation of disease. However, manual EMB interpretation has high inter-rater variability. Deep convolutional neural networks (CNNs) have been successfully applied to detect cancer, diabetic retinopathy, and dermatologic lesions from images. In this study, we develop a CNN classifier to detect clinical heart failure from H&E stained whole-slide images from a total of 209 patients, 104 patients were used for training and the remaining 105 patients for independent testing. The CNN was able to identify patients with heart failure or severe pathology with a 99% sensitivity and 94% specificity on the test set, outperforming conventional feature-engineering approaches. Importantly, the CNN outperformed two expert pathologists by nearly 20%. Our results suggest that deep learning analytics of EMB can be used to predict cardiac outcome.</p></div

Patient-level performance evaluation for predicting clinical heart failure or severe tissue pathology from H&E stained whole-slide images for the held-out test set.

<p>Patient-level performance evaluation for predicting clinical heart failure or severe tissue pathology from H&E stained whole-slide images for the held-out test set.</p

Receiver Operator Characteristic (ROC) curve for detection of clinical heart failure or severe tissue pathology.

<p>(a) ROC curve for image-level detection on the training dataset (DL vs. RF, p < 0.0001, two-sample Kolmogorov-Smirnov (KS) test). (b) ROC curve for patient-level detection on the training dataset (DL vs. RF, ns, KS test). (c) ROC curve for image-level detection on the held-out test dataset (DL vs. RF, p < 0.0001, KS test). (d) ROC curve for patient-level detection on the held-out test dataset (DL vs. RF, ns, KS test).</p

Patient-level performance evaluation for predicting clinical heart failure or severe tissue pathology from H&E stained whole-slide images for validation folds of the training data set.

<p>Patient-level performance evaluation for predicting clinical heart failure or severe tissue pathology from H&E stained whole-slide images for validation folds of the training data set.</p