Functional and developmental characterization of local motion sensing neurons in the fly visual system

Sighted animals use visual motion information to navigate in their environment, to search for food sources or mating partners and to avoid potentials predators. However, directional motion information is not explicitly represented in the photoreceptor signals, but rather needs to be extracted by postsynaptic circuits. For such a motion computation, different algorithmic models were proposed. The most prominent model multiplies the signal of two neighboring photoreceptors after one of them was temporally delayed. Fruit flies are well suited as a model organism to study the neuronal mechanisms underlying motion perception. With a low spatial but high temporal visual resolution, fruit flies are able to detect many different kinds of motion stimuli and perform a wide range of visually evoked behaviors. Thanks to the multitude of genetic tools optimized for Drosophila melanogaster, detailed manipulation of neuronal function can be performed on a molecular as well as on a cellular level. These tools allow to dissect the components of a neuronal circuit and investigate their respective function. In the visual system of flies exist neurons sensitive to wide field motion, which are important for the course control of flies. An open question remains the computation of upstream neurons detecting local motion. During my doctoral work I studied various aspects of the local motion sensing cells in the fly visual system: their functional properties, their importance for different behavioral tasks as well as their differentiation during development. In the first manuscript included in this thesis, we demonstrated that T4 and T5 cells are the elementary local motion sensing neurons of the fly. Calcium activity imaging of T4 and T5 cells revealed that four subtypes exist, each sensitive to motion along one of the four cardinal directions. Moreover, T4 cells responded specifically to light increments and T5 cells to light decrements. Blocking T4 neurons abolished the ON motion responses of postsynaptic lobula plate tangential cells. Accordingly, inactivating T5 cells inhibited the reaction of lobula plate tangential cells to OFF motion. We confirmed this effect by examining the turning behavior of walking flies with either T4 or T5 cells blocked. Flies without T4 output responded only to OFF edge motion, while flies with blocked T5 cells responded exclusively to ON edge motion. To investigate the functional role of the local motion sensing T4 and T5 cells, we studied the consequences of blocking these neurons and tested visual behavior. In the second manuscript, we described that inactivating T4 and T5 cells abolished the optomotor turning response of the flies. However, the motion blind flies were still able to orient towards a dark, vertical bar. Wedemonstrated that flies respond to the position of a bar independent of a motion cue. Therefore, we concluded that flies use positional as well as motion information to orient towards an attractive object. In the third manuscript, we further investigated the role of T4 and T5 cells in flight behavior and found these cells involved in the detection of expansion motion. Flight avoidance turns as well as landing responses of flies depend on functional T4 and T5 cells. These behaviors are evoked by expansion motion like a looming stimulus, which mimics an approaching predator or object. The importance of T4 and T5 cells for looming evoked behavior suggests, that these cells are not only connected to lobula plate tangential cells, which respond to rotatory wide-field motion, but are also presynaptic to looming sensitive neurons in the lobula plate. The last manuscript describes transcription factors important for the differentiation of T4 and T5 neurons. The morphology of all T4 and T5 subtypes is comparable; their dendrites are oriented opposite to the preferred direction of the cell and the axon terminals target one of the four lobula plate layers. Both the dendrites and the axon terminals are limited to only one layer of their respective neuropil. We found two postmitotic transcription factors expressed in the young T4 and T5 cells, SoxN and Sox102F, which regulate the common features of all subtypes. These transcription factors are crucial for the proper morphology of the T4 and T5 cells, as well as the function of the adult neurons

Functional and developmental characterization of local motion sensing neurons in the fly visual system

Sighted animals use visual motion information to navigate in their environment, to search for food sources or mating partners and to avoid potentials predators. However, directional motion information is not explicitly represented in the photoreceptor signals, but rather needs to be extracted by postsynaptic circuits. For such a motion computation, different algorithmic models were proposed. The most prominent model multiplies the signal of two neighboring photoreceptors after one of them was temporally delayed. Fruit flies are well suited as a model organism to study the neuronal mechanisms underlying motion perception. With a low spatial but high temporal visual resolution, fruit flies are able to detect many different kinds of motion stimuli and perform a wide range of visually evoked behaviors. Thanks to the multitude of genetic tools optimized for Drosophila melanogaster, detailed manipulation of neuronal function can be performed on a molecular as well as on a cellular level. These tools allow to dissect the components of a neuronal circuit and investigate their respective function. In the visual system of flies exist neurons sensitive to wide field motion, which are important for the course control of flies. An open question remains the computation of upstream neurons detecting local motion. During my doctoral work I studied various aspects of the local motion sensing cells in the fly visual system: their functional properties, their importance for different behavioral tasks as well as their differentiation during development. In the first manuscript included in this thesis, we demonstrated that T4 and T5 cells are the elementary local motion sensing neurons of the fly. Calcium activity imaging of T4 and T5 cells revealed that four subtypes exist, each sensitive to motion along one of the four cardinal directions. Moreover, T4 cells responded specifically to light increments and T5 cells to light decrements. Blocking T4 neurons abolished the ON motion responses of postsynaptic lobula plate tangential cells. Accordingly, inactivating T5 cells inhibited the reaction of lobula plate tangential cells to OFF motion. We confirmed this effect by examining the turning behavior of walking flies with either T4 or T5 cells blocked. Flies without T4 output responded only to OFF edge motion, while flies with blocked T5 cells responded exclusively to ON edge motion. To investigate the functional role of the local motion sensing T4 and T5 cells, we studied the consequences of blocking these neurons and tested visual behavior. In the second manuscript, we described that inactivating T4 and T5 cells abolished the optomotor turning response of the flies. However, the motion blind flies were still able to orient towards a dark, vertical bar. Wedemonstrated that flies respond to the position of a bar independent of a motion cue. Therefore, we concluded that flies use positional as well as motion information to orient towards an attractive object. In the third manuscript, we further investigated the role of T4 and T5 cells in flight behavior and found these cells involved in the detection of expansion motion. Flight avoidance turns as well as landing responses of flies depend on functional T4 and T5 cells. These behaviors are evoked by expansion motion like a looming stimulus, which mimics an approaching predator or object. The importance of T4 and T5 cells for looming evoked behavior suggests, that these cells are not only connected to lobula plate tangential cells, which respond to rotatory wide-field motion, but are also presynaptic to looming sensitive neurons in the lobula plate. The last manuscript describes transcription factors important for the differentiation of T4 and T5 neurons. The morphology of all T4 and T5 subtypes is comparable; their dendrites are oriented opposite to the preferred direction of the cell and the axon terminals target one of the four lobula plate layers. Both the dendrites and the axon terminals are limited to only one layer of their respective neuropil. We found two postmitotic transcription factors expressed in the young T4 and T5 cells, SoxN and Sox102F, which regulate the common features of all subtypes. These transcription factors are crucial for the proper morphology of the T4 and T5 cells, as well as the function of the adult neurons

DNA methylation-based prediction of response to immune checkpoint inhibition in metastatic melanoma

Background Therapies based on targeting immune checkpoints have revolutionized the treatment of metastatic melanoma in recent years. Still, biomarkers predicting long-term therapy responses are lacking. Methods A novel approach of reference-free deconvolution of large-scale DNA methylation data enabled us to develop a machine learning classifier based on CpG sites, specific for latent methylation components (LMC), that allowed for patient allocation to prognostic clusters. DNA methylation data were processed using reference-free analyses (MeDeCom) and reference-based computational tumor deconvolution (MethylCIBERSORT, LUMP). Results We provide evidence that DNA methylation signatures of tumor tissue from cutaneous metastases are predictive for therapy response to immune checkpoint inhibition in patients with stage IV metastatic melanoma. Conclusions These results demonstrate that LMC-based segregation of large-scale DNA methylation data is a promising tool for classifier development and treatment response estimation in cancer patients under targeted immunotherapy

The Lipid Receptor G2A (GPR132) Mediates Macrophage Migration in Nerve Injury-Induced Neuropathic Pain

Nerve injury-induced neuropathic pain is difficult to treat and mechanistically characterized by strong neuroimmune interactions, involving signaling lipids that act via specific G-protein coupled receptors. Here, we investigated the role of the signaling lipid receptor G2A (GPR132) in nerve injury-induced neuropathic pain using the robust spared nerve injury (SNI) mouse model. We found that the concentrations of the G2A agonist 9-HODE (9-Hydroxyoctadecadienoic acid) are strongly increased at the site of nerve injury during neuropathic pain. Moreover, G2A-deficient mice show a strong reduction of mechanical hypersensitivity after nerve injury. This phenotype is accompanied by a massive reduction of invading macrophages and neutrophils in G2A-deficient mice and a strongly reduced release of the proalgesic mediators TNFα, IL-6 and VEGF at the site of injury. Using a global proteome analysis to identify the underlying signaling pathways, we found that G2A activation in macrophages initiates MyD88-PI3K-AKT signaling and transient MMP9 release to trigger cytoskeleton remodeling and migration. We conclude that G2A-deficiency reduces inflammatory responses by decreasing the number of immune cells and the release of proinflammatory cytokines and growth factors at the site of nerve injury. Inhibiting the G2A receptor after nerve injury may reduce immune cell-mediated peripheral sensitization and may thus ameliorate neuropathic pain

DNA methylation-based prediction of response to immune checkpoint inhibition in metastatic melanoma

Background Therapies based on targeting immune checkpoints have revolutionized the treatment of metastatic melanoma in recent years. Still, biomarkers predicting long-term therapy responses are lacking.Methods A novel approach of reference-free deconvolution of large-scale DNA methylation data enabled us to develop a machine learning classifier based on CpG sites, specific for latent methylation components (LMC), that allowed for patient allocation to prognostic clusters. DNA methylation data were processed using reference-free analyses (MeDeCom) and reference-based computational tumor deconvolution (MethylCIBERSORT, LUMP).Results We provide evidence that DNA methylation signatures of tumor tissue from cutaneous metastases are predictive for therapy response to immune checkpoint inhibition in patients with stage IV metastatic melanoma.Conclusions These results demonstrate that LMC-based segregation of large-scale DNA methylation data is a promising tool for classifier development and treatment response estimation in cancer patients under targeted immunotherapy

Integrating Patient Data Into Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review

BackgroundRecent years have been witnessing a substantial improvement in the accuracy of skin cancer classification using convolutional neural networks (CNNs). CNNs perform on par with or better than dermatologists with respect to the classification tasks of single images. However, in clinical practice, dermatologists also use other patient data beyond the visual aspects present in a digitized image, further increasing their diagnostic accuracy. Several pilot studies have recently investigated the effects of integrating different subtypes of patient data into CNN-based skin cancer classifiers. ObjectiveThis systematic review focuses on the current research investigating the impact of merging information from image features and patient data on the performance of CNN-based skin cancer image classification. This study aims to explore the potential in this field of research by evaluating the types of patient data used, the ways in which the nonimage data are encoded and merged with the image features, and the impact of the integration on the classifier performance. MethodsGoogle Scholar, PubMed, MEDLINE, and ScienceDirect were screened for peer-reviewed studies published in English that dealt with the integration of patient data within a CNN-based skin cancer classification. The search terms skin cancer classification, convolutional neural network(s), deep learning, lesions, melanoma, metadata, clinical information, and patient data were combined. ResultsA total of 11 publications fulfilled the inclusion criteria. All of them reported an overall improvement in different skin lesion classification tasks with patient data integration. The most commonly used patient data were age, sex, and lesion location. The patient data were mostly one-hot encoded. There were differences in the complexity that the encoded patient data were processed with regarding deep learning methods before and after fusing them with the image features for a combined classifier. ConclusionsThis study indicates the potential benefits of integrating patient data into CNN-based diagnostic algorithms. However, how exactly the individual patient data enhance classification performance, especially in the case of multiclass classification problems, is still unclear. Moreover, a substantial fraction of patient data used by dermatologists remains to be analyzed in the context of CNN-based skin cancer classification. Further exploratory analyses in this promising field may optimize patient data integration into CNN-based skin cancer diagnostics for patients’ benefits

Effects of Label Noise on Deep Learning-Based Skin Cancer Classification

Recent studies have shown that deep learning is capable of classifying dermatoscopic images at least as well as dermatologists. However, many studies in skin cancer classification utilize non-biopsy-verified training images. This imperfect ground truth introduces a systematic error, but the effects on classifier performance are currently unknown. Here, we systematically examine the effects of label noise by training and evaluating convolutional neural networks (CNN) with 804 images of melanoma and nevi labeled either by dermatologists or by biopsy. The CNNs are evaluated on a test set of 384 images by means of 4-fold cross validation comparing the outputs with either the corresponding dermatological or the biopsy-verified diagnosis. With identical ground truths of training and test labels, high accuracies with 75.03% (95% CI: 74.39–75.66%) for dermatological and 73.80% (95% CI: 73.10–74.51%) for biopsy-verified labels can be achieved. However, if the CNN is trained and tested with different ground truths, accuracy drops significantly to 64.53% (95% CI: 63.12–65.94%, p < 0.01) on a non-biopsy-verified and to 64.24% (95% CI: 62.66–65.83%, p < 0.01) on a biopsy-verified test set. In conclusion, deep learning methods for skin cancer classification are highly sensitive to label noise and future work should use biopsy-verified training images to mitigate this problem

A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade.

Immune checkpoint inhibitors (ICIs) produce durable responses in some melanoma patients, but many patients derive no clinical benefit, and the molecular underpinnings of such resistance remain elusive. Here, we leveraged single-cell RNA sequencing (scRNA-seq) from 33 melanoma tumors and computational analyses to interrogate malignant cell states that promote immune evasion. We identified a resistance program expressed by malignant cells that is associated with T cell exclusion and immune evasion. The program is expressed prior to immunotherapy, characterizes cold niches in situ, and predicts clinical responses to anti-PD-1 therapy in an independent cohort of 112 melanoma patients. CDK4/6-inhibition represses this program in individual malignant cells, induces senescence, and reduces melanoma tumor outgrowth in mouse models in vivo when given in combination with immunotherapy. Our study provides a high-resolution landscape of ICI-resistant cell states, identifies clinically predictive signatures, and suggests new therapeutic strategies to overcome immunotherapy resistance