DART.2: bidirectional synaptic pharmacology with thousandfold cellular specificity.

Precision pharmacology aims to manipulate specific cellular interactions within complex tissues. In this pursuit, we introduce DART.2 (drug acutely restricted by tethering), a second-generation cell-specific pharmacology technology. The core advance is optimized cellular specificity-up to 3,000-fold in 15 min-enabling the targeted delivery of even epileptogenic drugs without off-target effects. Additionally, we introduce brain-wide dosing methods as an alternative to local cannulation and tracer reagents for brain-wide dose quantification. We describe four pharmaceuticals-two that antagonize excitatory and inhibitory postsynaptic receptors, and two that allosterically potentiate these receptors. Their versatility is showcased across multiple mouse-brain regions, including cerebellum, striatum, visual cortex and retina. Finally, in the ventral tegmental area, we find that blocking inhibitory inputs to dopamine neurons accelerates locomotion, contrasting with previous optogenetic and pharmacological findings. Beyond enabling the bidirectional perturbation of chemical synapses, these reagents offer intersectional precision-between genetically defined postsynaptic cells and neurotransmitter-defined presynaptic partners.

Knockdown siRNA Targeting GPR55 Reveals Significant Differences Between the Anti-inflammatory Actions of KLS-13019 and Cannabidiol.

KLS-13019 was reported previously to reverse paclitaxel-induced mechanical allodynia in a mouse model of chemotherapy-induced peripheral neuropathy (CIPN). Recent studies demonstrated that paclitaxel-induced increases in inflammatory markers (GPR55, NLRP3, and IL-1ß) of dorsal root ganglion (DRG) cultures were shown to be reversed by KLS-13019 treatment. The mechanism of action for KLS-13019-mediated reversal of paclitaxel-induced neuroinflammation now has been explored using GPR55 siRNA. Pre-treatment of DRG cultures with GPR55 siRNA produced a 21% decrease of immunoreactive (IR) area for GPR55 in cell bodies and a 59% decrease in neuritic IR area, as determined by high-content imaging. Using a 24-h reversal treatment paradigm, paclitaxel-induced increases in the inflammatory markers were reversed back to control levels after KLS-3019 treatment. Decreases in these inflammatory markers produced by KLS-13019 were significantly attenuated by GPR55 siRNA co-treatment, with mean IR area responses being attenuated by 56% in neurites and 53% in cell bodies. These data indicate that the percentage decreases in siRNA-mediated attenuation of KLS-13019-related efficacy on the inflammatory markers were similar to the percentage knockdown observed for neuritic GPR55 IR area. Similar studies conducted with cannabidiol (CBD), the parent compound of KLS-13019, produced low efficacy (25%) reversal of all inflammatory markers that were poorly attenuated (29%) by GPR55 siRNA. CBD was shown previously to be ineffective in reversing paclitaxel-induced mechanical allodynia. The present studies indicated significant differences between the anti-inflammatory properties of KLS-13019 and CBD which may play a role in their observed differences in the reversibility of mechanical allodynia in a mouse model of CIPN.

Knockdown siRNA targeting GPR55 reveals significant differences between the anti-inflammatory actions of KLS-13019 and cannabidiol.

KLS-13019 was reported previously to reverse paclitaxel-induced mechanical allodynia in a mouse model of chemotherapy-induced peripheral neuropathy (CIPN). Recent studies demonstrated that paclitaxel-induced increases in inflammatory markers (GPR55, NLRP3 and IL-1b) of dorsal root ganglion (DRG) cultures were shown to be reversed by KLS-13019 treatment. The mechanism of action for KLS-13019-mediated reversal of paclitaxel-induced neuroinflammation now has been explored using GPR55 siRNA. Pretreatment of DRG cultures with GPR55 siRNA produced a 21% decrease of immunoreactive (IR) area for GPR55 in cell bodies and a 59% decrease in neuritic IR area, as determined by high content imaging. Using a 24-hour reversal treatment paradigm, paclitaxel-induced increases in the inflammatory markers were reversed back to control levels after KLS-3019 treatment. Decreases in these inflammatory markers produced by KLS-13019 were significantly attenuated by GPR55 siRNA co-treatment, with mean IR area responses being attenuated by 56% in neurites and 53% in cell bodies. These data indicate that the percentage decreases in siRNA-mediated attenuation of KLS-13019-related efficacy on the inflammatory markers were similar to the percentage knockdown observed for neuritic GPR55 IR area. Similar studies conducted with cannabidiol (CBD), the parent compound of KLS-13019, produced low efficacy (25%) reversal of all inflammatory markers that were poorly attenuated (29%) by GPR55 siRNA. CBD was shown previously to be ineffective in reversing paclitaxel-induced mechanical allodynia. The present studies indicated significant differences between the anti-inflammatory properties of KLS-13019 and CBD which may play a role in their observed differences in the reversibility of mechanical allodynia in a mouse model of CIPN.

Global challenges in access to diagnostics and treatment for neuroendocrine tumor (NET) patients.

SCAN, an online survey, measured access to diagnosis, treatments and monitoring of neuroendocrine tumor (NET) patients globally. Between September and November 2019, NET patients and healthcare professionals (HCPs) completed an online, semi-standardized survey with 54 patient questions and 33 HCP questions. A total of 2359 patients with NETs and 436 HCPs responded. Misdiagnosis was common (44% [1043/2359]). Mean time to diagnosis was 4.8 years (standard deviation [SD], 6.2). Compared with global figures (60% [1407/2359]), the availability of 68 Ga-DOTA positron emission tomography (PET)/computed tomography (CT) was significantly lower in Asia (45% [126/280]) and higher in Oceania (86% [171/200]). HCPs reported that 68 Ga-DOTA PET/CT was free/affordable to fewer patients in Emerging and Developing Economies (EDE) than Advanced Economies (AE; 17% [26/150] and 59% [84/142], respectively). Compared with global data (52% [1234/2359]), patient-reported availability of peptide receptor radionuclide therapy (PRRT) was significantly lower in Asia (31% [88/280]) and higher in Oceania (61% [122/200]). Significant differences were observed in average annual NET specialist costs between AE and EDE ($1081 and $2915, respectively). Compared with AE, patients in EDE traveled further for NET specialists (1032 [SD, 1578] and 181 [SD, 496] km, respectively). Patients and HCPs both recommended referral to HCPs that were more knowledgeable in the field of NETs and had better access to NET experts/specialist centers. National care pathways, enhancing HCP NET knowledge and ensuring effective diagnostics and access to appropriate treatments are crucial to improving patient survival and NET care worldwide.

Efficient Syntheses of KLS-13019 Using Palladium Mediated Cross Couplings.

KLS-13019 is a structural analogue of cannabidiol, that shows improved bioavailability and potency in both preventing and reversing paclitaxel-induced neurotoxicity in vitro and in vivo. KLS-13019 was selected as a development candidate and attention was turned to development of a scalable synthesis. The original synthesis of KLS-13019 was not efficient, regioselective, or high yielding. Two new syntheses are reported that make use of the palladium catalyzed cross couplings to a chemically advanced intermediate 5, dramatically shortening (3-4 steps) and improving the overall yield. In addition, a convenient one pot Boc-cleavage and acetylation procedure is described to avoid impurities generated from a step-wise process.

Four-dimensional electrical resistivity imaging for monitoring pumping-induced saltwater intrusion in a coastal aquifer.

Conventional views of saltwater intrusion (SWI), where a basal saline wedge extends inland below fresh groundwater, can be complicated by the influence of saltwater cells in the upper part of aquifers in areas affected by tidal cycles. Distinguishing the contribution of each saltwater source may prove fundamental for well design and resource management. Application of time-lapse electrical resistivity imaging (ERI) during a 32-h pumping test in a pristine unconfined coastal sand aquifer, affected by strong tidal ranges (>2 m), aimed to evaluate the potential of the method to characterize the source of induced SWI in four dimensions (three dimensions and time). Water level monitoring during the test revealed that at the end of pumping, the upper 2 m of the aquifer had dewatered in the vicinity of the well field, reversing hydraulic gradients between the aquifer and the sea. This induced SI, with mixing models of well head water quality suggesting that saline water contributions to total discharge rose from 4 % to 8 %. ERI results reflected dewatering through an increase in resistivity in the upper 2-6 m of the aquifer, while a decline in resistivity, relative to background conditions, occurred immediately below this, reflecting the migration of saline water through the upper layers of the aquifer to the pumping well. By contrast no change in resistivity occurred at depth, indicating no significant change in contribution from the basal saline water to discharge. Test findings suggest that future water resource development at the site should focus on close monitoring of shallow pumping, or pumping from deeper parts of the aquifer, while more generally demonstrating the value of time-lapse geophysical methods in informing coastal water resource management.

PRESTO 2: An International Survey to Evaluate Patients' Injection Experiences with the Latest Devices/Formulations of Long-Acting Somatostatin Analog Therapies for Neuroendocrine Tumors or Acromegaly.

INTRODUCTION: Real-world data evaluating patients' injection experiences using the latest devices/formulations of the long-acting (LA) somatostatin analogs (SSAs) lanreotide Autogel/Depot (LAN; Somatuline®) and octreotide LA release (OCT; Sandostatin®) are limited. METHODS: PRESTO 2 was a 2020/2021 e-survey comparing injection experience of adults with neuroendocrine tumors (NETs) or acromegaly treated with LAN prefilled syringe versus OCT syringe for > 3 months in Canada, Ireland, the UK and the USA (planned sample size, 304). PRIMARY ENDPOINT: the proportion of patients with injection-site pain lasting > 2 days after their most recent injection, analyzed using a multivariate logistic regression model. Secondary endpoints included interference with daily life due to injection-site pain and technical injection problems in patients with current SSA use for ≥ 6 months. RESULTS: There were 304 respondents (acromegaly, n = 85; NETs, n = 219; LAN, n = 168; OCT, n = 136; 69.2% female; mean age, 59.6 years). Fewer patients had injection-site pain lasting > 2 days after the most recent injection with LAN (6.0%) than OCT (22.8%); the odds of pain lasting > 2 days were significantly lower for LAN than OCT, adjusted for disease subgroup and occurrence of injection-site reactions (odds ratio [95% confidence interval]: 0.13 [0.06-0.30]; p < 0.0001). Injection-site pain interfered with daily life "a little bit" or "quite a bit" in 37.2% and 3.8% (LAN) versus 52.5% and 7.5% (OCT) of patients, respectively. Among patients with ≥ 6 months' experience with current SSA (92.4% of patients), technical injection problems never occurred in 76.8% (LAN) and 42.9% (OCT) of patients. CONCLUSIONS: Compared with OCT, significantly fewer patients using LAN had injection-site pain lasting > 2 days after their most recent injection. Also, fewer LAN-treated patients experienced technical problems during injection. These findings demonstrate the importance of injection modality for overall LA SSA injection experience for patients with acromegaly or NETs.

Patients with neuroendocrine tumors or acromegaly often receive long-term monthly treatment with somatostatin analogs. These injectable drugs stop the body from making an excess of certain hormones. Understanding patients' experiences of these injections helps to provide better care. The PRESTO 2 online study surveyed 304 patients in Canada, Ireland, the UK and the USA with neuroendocrine tumors or acromegaly who were being treated with a somatostatin analog, either lanreotide Autogel/Depot (LAN) or octreotide long-acting release (OCT). The survey asked about injection experience, including injection-site pain lasting > 2 days and how it affected patients' lives, anxiety before injections and technical problems during injections (like syringe blockages). The survey showed fewer patients receiving LAN than OCT had injection-site pain that lasted > 2 days, and fewer said that the pain interfered with their daily lives. There were fewer technical injection problems with LAN than with OCT. However, more patients receiving LAN than OCT felt anxious before their injection. In some countries (including Canada, Ireland and the UK, but not the USA), the patient (or family member/friend) can inject LAN if they are on a stable dose, their doctor agrees, and they received training. A nurse/doctor must inject OCT. In PRESTO 2, about 40% of non-US patients who were eligible injected themselves (or were helped by a family member/friend). This may explain why more patients reported anxiety in the LAN group. PRESTO 2 provides important insights into patients' experiences of receiving somatostatin analogs and helps identify areas for improving patient care.

Anti-Inflammatory Properties of KLS-13019: a Novel GPR55 Antagonist for Dorsal Root Ganglion and Hippocampal Cultures.

KLS-13019, a novel devised cannabinoid-like compound, was explored for anti-inflammatory actions in dorsal root ganglion cultures relevant to chemotherapy-induced peripheral neuropathy (CIPN). Time course studies with 3 µM paclitaxel indicated > 1.9-fold increases in immunoreactive (IR) area for cell body GPR55 after 30 min as determined by high content imaging. To test for reversibility of paclitaxel-induced increases in GPR55, cultures were treated for 8 h with paclitaxel alone and then a dose response to KLS-13019 added for another 16 h. This "reversal" paradigm indicated established increases in cell body GPR55 IR areas were decreased back to control levels. Because GPR55 had previously reported inflammatory actions, IL-1ß and NLRP3 (inflammasome-3 marker) were also measured in the "reversal" paradigm. Significant increases in all inflammatory markers were produced after 8 h of paclitaxel treatment alone that were reversed to control levels with KLS-13019 treatment. Accompanying studies using alamar blue indicated that decreased cellular viability produced by paclitaxel treatment was reverted back to control levels by KLS-13019. Similar studies conducted with lysophosphatidylinositol (GPR55 agonist) in DRG or hippocampal cultures demonstrated significant increases in neuritic GPR55, NLRP3 and IL-1ß areas that were reversed to control levels with KLS-13019 treatment. Studies with a human GPR55-ß-arrestin assay in Discover X cells indicated that KLS-13019 was an antagonist without agonist activity. These studies indicated that KLS-13019 has anti-inflammatory properties mediated through GPR55 antagonist actions. Together with previous studies, KLS-13019 is a potent neuroprotective, anti-inflammatory cannabinoid with therapeutic potential for high efficacy treatment of neuropathic pain.

Combining machine learning and conventional statistical approaches for risk factor discovery in a large cohort study.

We present a simple and efficient hypothesis-free machine learning pipeline for risk factor discovery that accounts for non-linearity and interaction in large biomedical databases with minimal variable pre-processing. In this study, mortality models were built using gradient boosting decision trees (GBDT) and important predictors were identified using a Shapley values-based feature attribution method, SHAP values. Cox models controlled for false discovery rate were used for confounder adjustment, interpretability, and further validation. The pipeline was tested using information from 502,506 UK Biobank participants, aged 37-73 years at recruitment and followed over seven years for mortality registrations. From the 11,639 predictors included in GBDT, 193 potential risk factors had SHAP values ≥ 0.05, passed the correlation test, and were selected for further modelling. Of the total variable importance summed up, 60% was directly health related, and baseline characteristics, sociodemographics, and lifestyle factors each contributed about 10%. Cox models adjusted for baseline characteristics, showed evidence for an association with mortality for 166 out of the 193 predictors. These included mostly well-known risk factors (e.g., age, sex, ethnicity, education, material deprivation, smoking, physical activity, self-rated health, BMI, and many disease outcomes). For 19 predictors we saw evidence for an association in the unadjusted but not adjusted analyses, suggesting bias by confounding. Our GBDT-SHAP pipeline was able to identify relevant predictors 'hidden' within thousands of variables, providing an efficient and pragmatic solution for the first stage of hypothesis free risk factor identification.

Cancer Tissue Classification Using Supervised Machine Learning Applied to MALDI Mass Spectrometry Imaging.

Matrix assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) can determine the spatial distribution of analytes such as protein distributions in a tissue section according to their mass-to-charge ratio. Here, we explored the clinical potential of machine learning (ML) applied to MALDI MSI data for cancer diagnostic classification using tissue microarrays (TMAs) on 302 colorectal (CRC) and 257 endometrial cancer (EC)) patients. ML based on deep neural networks discriminated colorectal tumour from normal tissue with an overall accuracy of 98% in balanced cross-validation (98.2% sensitivity and 98.6% specificity). Moreover, our machine learning approach predicted the presence of lymph node metastasis (LNM) for primary tumours of EC with an accuracy of 80% (90% sensitivity and 69% specificity). Our results demonstrate the capability of MALDI MSI for complementing classic histopathological examination for cancer diagnostic applications.

Unifying information theory and machine learning in a model of electrode discrimination in cochlear implants.

Despite the development and success of cochlear implants over several decades, wide inter-subject variability in speech perception is reported. This suggests that cochlear implant user-dependent factors limit speech perception at the individual level. Clinical studies have demonstrated the importance of the number, placement, and insertion depths of electrodes on speech recognition abilities. However, these do not account for all inter-subject variability and to what extent these factors affect speech recognition abilities has not been studied. In this paper, an information theoretic method and machine learning technique are unified in a model to investigate the extent to which key factors limit cochlear implant electrode discrimination. The framework uses a neural network classifier to predict which electrode is stimulated for a given simulated activation pattern of the auditory nerve, and mutual information is then estimated between the actual stimulated electrode and predicted ones. We also investigate how and to what extent the choices of parameters affect the performance of the model. The advantages of this framework include i) electrode discrimination ability is quantified using information theory, ii) it provides a flexible framework that may be used to investigate the key factors that limit the performance of cochlear implant users, and iii) it provides insights for future modeling studies of other types of neural prostheses.

The quest for better clinical word vectors: Ontology based and lexical vector augmentation versus clinical contextual embeddings.

BACKGROUND: Word vectors or word embeddings are n-dimensional representations of words and form the backbone of Natural Language Processing of textual data. This research experiments with algorithms that augment word vectors with lexical constraints that are popular in NLP research and clinical domain constraints derived from the Unified Medical Language System (UMLS). It also compares the performance of the augmented vectors with Bio + Clinical BERT vectors which have been trained and fine-tuned on clinical datasets. METHODS: Word2vec vectors are generated for words in a publicly available de-identified Electronic Health Records (EHR) dataset and augmented by ontologies using three algorithms that have fundamentally different approaches to vector augmentation. The augmented vectors are then evaluated alongside publicly available Bio + Clinical BERT on their correlation with human-annotated lists using Spearman's correlation coefficient. They are also evaluated on the downstream task of Named Entity Recognition (NER). Quantitative and empirical evaluations are used to highlight the strengths and weaknesses of the different approaches. RESULTS: The counter-fitted word2vec vectors augmented with information from the UMLS ontology produced the best correlation overall with human-annotated evaluation lists (Spearman's correlation of 0.733 with mini mayo-doctors' annotation) while Bio + Clinical BERT produces the best results in the NER task (F1 of 0.87 and 0.811 on the i2b2 2010 and i2b2 2012 datasets respectively) in our experiments. CONCLUSION: Clinically adapted word2vec vectors successfully encapsulate concepts of lexical and clinical synonymy and antonymy and to a smaller extent, hyponymy and hypernymy. Bio + Clinical BERT vectors perform better at NER and avoid out-of-vocabulary words.

Machine Learning Quantitation of Cardiovascular and Cerebrovascular Disease: A Systematic Review of Clinical Applications.

Research into machine learning (ML) for clinical vascular analysis, such as those useful for stroke and coronary artery disease, varies greatly between imaging modalities and vascular regions. Limited accessibility to large diverse patient imaging datasets, as well as a lack of transparency in specific methods, are obstacles to further development. This paper reviews the current status of quantitative vascular ML, identifying advantages and disadvantages common to all imaging modalities. Literature from the past 8 years was systematically collected from MEDLINE® and Scopus database searches in January 2021. Papers satisfying all search criteria, including a minimum of 50 patients, were further analysed and extracted of relevant data, for a total of 47 publications. Current ML image segmentation, disease risk prediction, and pathology quantitation methods have shown sensitivities and specificities over 70%, compared to expert manual analysis or invasive quantitation. Despite this, inconsistencies in methodology and the reporting of results have prevented inter-model comparison, impeding the identification of approaches with the greatest potential. The clinical potential of this technology has been well demonstrated in Computed Tomography of coronary artery disease, but remains practically limited in other modalities and body regions, particularly due to a lack of routine invasive reference measurements and patient datasets.

A deep convolutional neural network for segmentation of whole-slide pathology images identifies novel tumour cell-perivascular niche interactions that are associated with poor survival in glioblastoma.

BACKGROUND: Glioblastoma is the most aggressive type of brain cancer with high-levels of intra- and inter-tumour heterogeneity that contribute to its rapid growth and invasion within the brain. However, a spatial characterisation of gene signatures and the cell types expressing these in different tumour locations is still lacking. METHODS: We have used a deep convolutional neural network (DCNN) as a semantic segmentation model to segment seven different tumour regions including leading edge (LE), infiltrating tumour (IT), cellular tumour (CT), cellular tumour microvascular proliferation (CTmvp), cellular tumour pseudopalisading region around necrosis (CTpan), cellular tumour perinecrotic zones (CTpnz) and cellular tumour necrosis (CTne) in digitised glioblastoma histopathological slides from The Cancer Genome Atlas (TCGA). Correlation analysis between segmentation results from tumour images together with matched RNA expression data was performed to identify genetic signatures that are specific to different tumour regions. RESULTS: We found that spatially resolved gene signatures were strongly correlated with survival in patients with defined genetic mutations. Further in silico cell ontology analysis along with single-cell RNA sequencing data from resected glioblastoma tissue samples showed that these tumour regions had different gene signatures, whose expression was driven by different cell types in the regional tumour microenvironment. Our results further pointed to a key role for interactions between microglia/pericytes/monocytes and tumour cells that occur in the IT and CTmvp regions, which may contribute to poor patient survival. CONCLUSIONS: This work identified key histopathological features that correlate with patient survival and detected spatially associated genetic signatures that contribute to tumour-stroma interactions and which should be investigated as new targets in glioblastoma. The source codes and datasets used are available in GitHub: https://github.com/amin20/GBM_WSSM .

The Application of Deep Convolutional Neural Networks to Brain Cancer Images: A Survey.

In recent years, improved deep learning techniques have been applied to biomedical image processing for the classification and segmentation of different tumors based on magnetic resonance imaging (MRI) and histopathological imaging (H&E) clinical information. Deep Convolutional Neural Networks (DCNNs) architectures include tens to hundreds of processing layers that can extract multiple levels of features in image-based data, which would be otherwise very difficult and time-consuming to be recognized and extracted by experts for classification of tumors into different tumor types, as well as segmentation of tumor images. This article summarizes the latest studies of deep learning techniques applied to three different kinds of brain cancer medical images (histology, magnetic resonance, and computed tomography) and highlights current challenges in the field for the broader applicability of DCNN in personalized brain cancer care by focusing on two main applications of DCNNs: classification and segmentation of brain cancer tumors images.

The discovery of azetidine-piperazine di-amides as potent, selective and reversible monoacylglycerol lipase (MAGL) inhibitors.

Monoacylglycerol lipase (MAGL) is the enzyme that is primarily responsible for hydrolyzing the endocannabinoid 2-arachidononylglycerol (2-AG) to arachidonic acid (AA). It has emerged in recent years as a potential drug target for a number of diseases. Herein, we report the discovery of compound 6g from a series of azetidine-piperazine di-amide compounds as a potent, selective, and reversible inhibitor of MAGL. Oral administration of compound 6g increased 2-AG levels in rat brain and produced full efficacy in the rat complete Freund's adjuvant (CFA) model of inflammatory pain.

Characterization of Young and Old Adult Brains: An EEG Functional Connectivity Analysis.

Brain connectivity studies have reported that functional networks change with older age. We aim to (1) investigate whether electroencephalography (EEG) data can be used to distinguish between individual functional networks of young and old adults; and (2) identify the functional connections that contribute to this classification. Two eyes-open resting-state EEG recording sessions with 64 electrodes for each of 22 younger adults (19-37 years) and 22 older adults (63-85 years) were conducted. For each session, imaginary coherence matrices in delta, theta, alpha, beta and gamma bands were computed. A range of machine learning classification methods were utilized to distinguish younger and older adult brains. A support vector machine (SVM) classifier was 93% accurate in classifying the brains by age group. We report decreased functional connectivity with older age in delta, theta, alpha and gamma bands, and increased connectivity with older age in beta band. Most connections involving frontal, temporal, and parietal electrodes, and more than half of connections involving occipital electrodes, showed decreased connectivity with older age. Slightly less than half of the connections involving central electrodes showed increased connectivity with older age. Functional connections showing decreased strength with older age were not significantly different in electrode-to-electrode distance than those that increased with older age. Most of the connections used by the classifier to distinguish participants by age group belonged to the alpha band. Findings suggest a decrease in connectivity in key networks and frequency bands associated with attention and awareness, and an increase in connectivity of the sensorimotor functional networks with aging during a resting state.

A roadmap for urban evolutionary ecology.

Urban ecosystems are rapidly expanding throughout the world, but how urban growth affects the evolutionary ecology of species living in urban areas remains largely unknown. Urban ecology has advanced our understanding of how the development of cities and towns change environmental conditions and alter ecological processes and patterns. However, despite decades of research in urban ecology, the extent to which urbanization influences evolutionary and eco-evolutionary change has received little attention. The nascent field of urban evolutionary ecology seeks to understand how urbanization affects the evolution of populations, and how those evolutionary changes in turn influence the ecological dynamics of populations, communities, and ecosystems. Following a brief history of this emerging field, this Perspective article provides a research agenda and roadmap for future research aimed at advancing our understanding of the interplay between ecology and evolution of urban-dwelling organisms. We identify six key questions that, if addressed, would significantly increase our understanding of how urbanization influences evolutionary processes. These questions consider how urbanization affects nonadaptive evolution, natural selection, and convergent evolution, in addition to the role of urban environmental heterogeneity on species evolution, and the roles of phenotypic plasticity versus adaptation on species' abundance in cities. Our final question examines the impact of urbanization on evolutionary diversification. For each of these six questions, we suggest avenues for future research that will help advance the field of urban evolutionary ecology. Lastly, we highlight the importance of integrating urban evolutionary ecology into urban planning, conservation practice, pest management, and public engagement.