Machine Learning-Based Classification of Transcriptome Signatures of Non-Ulcerative Bladder Pain Syndrome.

Lower urinary tract dysfunction (LUTD) presents a global health challenge with symptoms impacting a substantial percentage of the population. The absence of reliable biomarkers complicates the accurate classification of LUTD subtypes with shared symptoms such as non-ulcerative Bladder Pain Syndrome (BPS) and overactive bladder caused by bladder outlet obstruction with Detrusor Overactivity (DO). This study introduces a machine learning (ML)-based approach for the identification of mRNA signatures specific to non-ulcerative BPS. Using next-generation sequencing (NGS) transcriptome data from bladder biopsies of patients with BPS, benign prostatic obstruction with DO, and controls, our statistical approach successfully identified 13 candidate genes capable of discerning BPS from control and DO patients. This set was validated using Quantitative Polymerase Chain Reaction (QPCR) in a larger patient cohort. To confirm our findings, we applied both supervised and unsupervised ML approaches to the QPCR dataset. A three-mRNA signature TPPP3, FAT1, and NCALD, emerged as a robust classifier for non-ulcerative BPS. The ML-based framework used to define BPS classifiers establishes a solid foundation for comprehending the gene expression changes in the bladder during BPS and serves as a valuable resource and methodology for advancing signature identification in other fields. The proposed ML pipeline demonstrates its efficacy in handling challenges associated with limited sample sizes, offering a promising avenue for applications in similar domains.

Enhancing Capacity of Community-Academic Partnerships to Achieve Health Equity: Results From the CBPR Partnership Academy.

Community-based participatory research (CBPR) is an equitable partnership approach that links academic researchers, community organizations, and public health practitioners to work together to understand and address health inequities. Although numerous educational materials on CBPR exist, few training programs develop the skills and knowledge needed to establish effective, equitable partnerships. Furthermore, there are few professional development opportunities for academic researchers, practitioners, and community members to obtain these competencies in an experiential co-learning process. In response, the Detroit Community-Academic Urban Research Center developed the CBPR Partnership Academy, an innovative, yearlong capacity-building program facilitated by experienced community and academic partners, involving an intensive short course, partnership development, grant proposal preparation and funding, mentoring, online learning forums, and networking. Three diverse cohorts (36 teams) from 18 states and 2 tribal nations have participated. We describe the rationale and components of the training program and present results from the first two cohorts. Evaluation results suggest enhanced competence and efficacy in conducting CBPR. Outcomes include partnerships established, grant proposals submitted and funded, workshops and research conducted, and findings disseminated. A community-academic partner-based, integrated, applied program can be effective for professional development and establishing innovative linkages between academics and practitioners aimed at achieving health equity.

Whole-exome sequencing of over 4100 men of African ancestry and prostate cancer risk.

Prostate cancer is the most common non-skin cancer in males, with a ∼1.5-2-fold higher incidence in African American men when compared with whites. Epidemiologic evidence supports a large heritable contribution to prostate cancer, with over 100 susceptibility loci identified to date that can explain ∼33% of the familial risk. To explore the contribution of both rare and common variation in coding regions to prostate cancer risk, we sequenced the exomes of 2165 prostate cancer cases and 2034 controls of African ancestry at a mean coverage of 10.1×. We identified 395 220 coding variants down to 0.05% frequency [57% non-synonymous (NS), 42% synonymous and 1% gain or loss of stop codon or splice site variant] in 16 751 genes with the strongest associations observed in SPARCL1 on 4q22.1 (rs13051, Ala49Asp, OR = 0.78, P = 1.8 × 10(-6)) and PTPRR on 12q15 (rs73341069, Val239Ile, OR = 1.62, P = 2.5 × 10(-5)). In gene-level testing, the two most significant genes were C1orf100 (P = 2.2 × 10(-4)) and GORAB (P = 2.3 × 10(-4)). We did not observe exome-wide significant associations (after correcting for multiple hypothesis testing) in single variant or gene-level testing in the overall case-control or case-case analyses of disease aggressiveness. In this first whole-exome sequencing study of prostate cancer, our findings do not provide strong support for the hypothesis that NS coding variants down to 0.5-1.0% frequency have large effects on prostate cancer risk in men of African ancestry. Higher-coverage sequencing efforts in larger samples will be needed to study rarer variants with smaller effect sizes associated with prostate cancer risk.

Linear doggybone DNA vaccine induces similar immunological responses to conventional plasmid DNA independently of immune recognition by TLR9 in a pre-clinical model.

Vaccination with DNA that encodes cancer antigens is a simple and convenient way to raise immunity against cancer and has already shown promise in the clinical setting. Conventional plasmid DNA is commonly used which together with the encoded antigen also includes bacterial immunostimulatory CpG motifs to target the DNA sensor Toll-like receptor 9. Recently DNA vaccines using doggybone DNA (dbDNA™), have been developed without the use of bacteria. The cell-free process relies on the use of Phi29 DNA polymerase to amplify the template followed by protelomerase TelN to complete individual closed linear DNA. The resulting DNA contains the required antigenic sequence, a promoter and a poly A tail but lacks bacterial sequences such as an antibiotic resistance gene, prompting the question of immunogenicity. Here we compared the ability of doggybone DNA vaccine with plasmid DNA vaccine to induce adaptive immunity using clinically relevant oncotargets E6 and E7 from HPV. We demonstrate that despite the inability to trigger TLR9, doggybone DNA was able to induce similar levels of cellular and humoral immunity as plasmid DNA, with suppression of established TC-1 tumours.

Linked CD4 T Cell Help: Broadening Immune Attack Against Cancer by Vaccination.

In the last decade, immunotherapy with monoclonal antibodies targeting immunological check points has become a breakthrough therapeutic modality for solid cancers. However, only up to 50 % of patients benefit from this powerful approach. For others vaccination might provide a plausible addition or alternative. For induction of effective anticancer immunity CD4+ T cell help is required, which is often difficult to induce to self cancer targets because of tolerogenic mechanisms. Our approach for cancer vaccines has been to incorporate into the vaccine design sequences able to activate foreign T cell help, through genetically linking cancer targets to microbial sequences (King et al. in Nat Med 4(11):1281-1286, 1998; Savelyeva et al. in Nat Biotechnol 19(8):760-764, 2001). This harnesses the non-tolerized CD4 T cell repertoire available in patients to help induction of effective immunity against fused cancer antigens. Multiple immune effector mechanisms including antibody, CD8+ T cells as well as CD4 effector T cells can be activated using this strategy. Delivery via DNA vaccines has already indicated clinical efficacy. The same principle of linked T cell help has now been transferred to other novel vaccine modalities to further potentiate immunity against cancer targets.

Is State Anxiety, Trait Anxiety, or Anxiety Sensitivity a Clinical Predictor of Symptoms in Those Presenting With Mild Traumatic Brain Injury or Concussion?

Clinical Scenario: Mild traumatic brain injury, or concussion, has been associated with physical, cognitive, and emotional sequelae. Little is understood in regard to many characteristics, such as anxiety, and their effect on post-concussion symptoms. CLINICAL QUESTION: Is state anxiety, trait anxiety, or anxiety sensitivity a clinical predictor of symptoms in those presenting with mild traumatic brain injury or concussion? Summary of Key Findings: A literature search returned 3 possible studies; 3 studies met inclusion criteria and included. One study reported in athletes that greater social support was associated with decreased state-anxiety, lower state anxiety post-concussion was associated with increased social support, and that those with greater social support may experience reduced anxiety, regardless of injury type sustained. One study reported baseline trait anxiety in athletes was not significantly associated with post-concussion state anxiety, but that symptoms of depression at baseline was the strongest predictor for post-concussion state anxiety. Three studies reported that state and trait anxiety are not related to increased post-concussion symptom scores. One study reported that greater anxiety sensitivity is related to higher reported post-concussion symptom scores, which may manifest as somatic symptoms following concussion, and revealed that anxiety sensitivity may be a risk factor symptom development. Clinical Bottom Line: There is low-level to moderate evidence to support that anxiety sensitivity is linked to post-concussion symptoms. State and trait anxiety do not appear to be related to post-concussion symptoms alone. Post-concussion state anxiety may occur if post-concussion symptoms of depression are present or if baseline symptoms of depression are present. Better social support may improve state anxiety post-concussion. Strength of Recommendation: There is grade B evidence to support that state and trait anxiety are not risk factors for post-concussion symptom development. There is grade C evidence to support anxiety sensitivity as a risk factor for developing post-concussion symptoms.

The outcome of B-cell receptor signaling in chronic lymphocytic leukemia: proliferation or anergy.

Biologists and clinicians agree that the B-cell receptor influences the behavior of chronic lymphocytic leukemia, and promising new drugs are aimed at receptor-associated kinases. Engagement of surface immunoglobulin by antigen is a key driver of malignant cells with outcome influenced by the nature of the cell, the level of stimulation and the microenvironment. Analysis of surface immunoglobulin-mediated signaling in the two major disease subsets defined by IGHV mutational status reveals bifurcation of responses toward proliferation or anergy. Mutated chronic lymphocytic leukemia, generally of relatively good prognosis, is mainly, but not exclusively, driven towards anergy in vivo. In contrast, unmutated chronic lymphocytic leukemia shows less evidence for anergy in vivo retaining more responsiveness to surface immunoglobulin M-mediated signaling, possibly explaining increased tumor progression. Expression and function of surface immunoglobulin M in unmutated chronic lymphocytic leukemia appear rather homogeneous, but mutated chronic lymphocytic leukemia exhibits a highly heterogeneous profile that may relate to further variable clinical behavior within this subset. Anergy should increase susceptibility to apoptosis but, in leukemic cells, this may be countered by overexpression of the B-cell lymphoma-2 survival protein. Maintained anergy spreads to chemokines and adhesion molecules, restraining homing and migration. However, anergy is not necessarily completely benign, being able to reverse and regenerate surface immunoglobulin M-mediated responses. A two-pronged attack on proliferative and anti-apoptotic pathways may succeed. Increased understanding of how chronic lymphocytic leukemia cells are driven to anergy or proliferation should reveal predictive biomarkers of progression and of likely response to kinase inhibitors, which could assist therapeutic decisions.

Healthy aging in relation to chronic pain and quality of life in Europe.

OBJECTIVE: To undertake a review of the most recent data on the relationship between quality of life (QoL) and chronic pain, as a basis for discussions about healthy aging in Europe. METHOD: A search was conducted to obtain studies on the relationship between pain severity and QoL and intervention studies reporting both QoL and pain severity in those with chronic pain in Europe. Medline and Embase were searched for observational studies and systematic reviews from 2009 to 2011. Four further databases were searched for systematic reviews and guidance from 2005 to 2011. Update searches for observational studies and systematic reviews for the period November 2011 to January 2013 were performed on Medline and Embase. RESULTS: We identified 8 observational studies and 1 systematic review that generally showed a statistically significant relationship between pain severity and QoL. We identified 5 systematic reviews of interventions in chronic pain that summarized both pain and QoL data that generally showed both a statistically significant reduction in pain and statistically significant increase in QoL. CONCLUSION: There is strong evidence of a correlation between pain severity and QoL. There is some evidence that treatment in chronic pain patients can reduce pain and simultaneously improve QoL. Prevention and treatment of chronic pain may be of significant help in reaching the aim to increase the healthy lifespan.

Effectiveness of autumn 2023 COVID-19 vaccination and residual protection of prior doses against hospitalisation in England, estimated using a test-negative case-control study.

INTRODUCTION: The last COVID-19 vaccine offered to all adults in England became available from November 2021. The most recent booster programme commenced in September 2023. Bivalent BA.4-5 or monovalent XBB.1.5 boosters were given. During the study period, the JN.1 variant became dominant in England. METHODS: Vaccine effectiveness against hospitalisation was estimated throughout using the test-negative case-control study design where positive PCR tests from hospitalised individuals are cases and comparable negative PCR tests are controls. Multivariable logistic regression was used to assess vaccine effectiveness against hospitalisation with the test result as the outcome, vaccination status as the primary exposure variable of interest and confounder adjustment. RESULTS: There was no evidence of residual protection for boosters given as part of previous campaigns. There were 28,916 eligible tests included to estimate the effectiveness of the autumn 2023 boosters in those aged 65 years and older. VE peaked at 50.6% (95% CI: 44.2-56.3%) after 2-4 weeks, followed by waning to 13.6% (95% CI: -11.7 to 33.2%). Estimates were generally higher for the XBB.1.5 booster than the BA.4-5 booster, but this difference was not statistically significant. Point estimates were highest against XBB sub-lineages. Effectiveness was lower against both JN.1 and EG.5.1 variants with confidence intervals non-overlapping with the effectiveness of the XBB sub-lineages at 2-4 weeks for EG.5.1 where VE was 44.5% (95% CI: 20.2-61.4%) and at 5-9 weeks for JN.1 where VE was 26.4% (95%CI: -3.4 to 47.6%). CONCLUSIONS: The recent monovalent XBB.1.5 and bivalent BA.4-5 boosters provided comparable and good protection against hospitalisation, however there was evidence of lower VE against hospitalisation of these boosters against JN.1.

Machine Learning-based Classification of transcriptome Signatures of non-ulcerative Bladder Pain Syndrome.

Lower urinary tract dysfunction (LUTD) presents a global health challenge with symptoms impacting a substantial percentage of the population. The absence of reliable biomarkers complicates the accurate classification of LUTD subtypes with shared symptoms such as non-ulcerative Bladder Pain Syndrome (BPS) and overactive bladder caused by bladder outlet obstruction with Detrusor Overactivity (DO). This study introduces a machine learning (ML)-based approach for the identification of mRNA signatures specific to non-ulcerative BPS. Using next-generation sequencing (NGS) transcriptome data from bladder biopsies of patients with BPS, benign prostatic obstruction with DO and controls, our statistical approach successfully identified 13 candidate genes capable of discerning BPS from control and DO patients. This set was subsequently validated using Quantitative Polymerase Chain Reaction (QPCR) in a larger patient cohort. To confirm our findings, we applied both supervised and unsupervised ML approaches to the QPCR dataset. Notably, a three-mRNA signature TPPP3, FAT1, and NCALD, emerged as a robust classifier, effectively distinguishing patients with non-ulcerative BPS from controls and patients with DO. This signature was universally selected by both supervised and unsupervised approaches. The ML-based framework used to define BPS classifiers not only establishes a solid foundation for comprehending the specific gene expression changes in the bladder of the patients with BPS but also serves as a valuable resource and methodology for advancing signature identification in other fields. The proposed ML pipeline demonstrates its efficacy in handling challenges associated with limited sample sizes, offering a promising avenue for applications in similar domains.

A systematic community-based participatory approach to refining an evidence-based community-level intervention: the HOLA intervention for Latino men who have sex with men.

Our community-based participatory research partnership engaged in a multistep process to refine a culturally congruent intervention that builds on existing community strengths to promote sexual health among immigrant Latino men who have sex with men (MSM). The steps were the following: (1) increase Latino MSM participation in the existing partnership, (2) establish an Intervention Team, (3) review the existing sexual health literature, (4) explore needs and priorities of Latino MSM, (5) narrow priorities based on what is important and changeable, (6) blend health behavior theory with Latino MSM's lived experiences, (7) design an intervention conceptual model, (8) develop training modules and (9) resource materials, and (10) pretest and (11) revise the intervention. The developed intervention contains four modules to train Latino MSM to serve as lay health advisors known as Navegantes. These modules synthesize locally collected data with other local and national data; blend health behavior theory, the lived experiences, and cultural values of immigrant Latino MSM; and harness the informal social support Latino MSM provide one another. This community-level intervention is designed to meet the expressed sexual health priorities of Latino MSM. It frames disease prevention within sexual health promotion.

Novel phenotype characterization utilizing electrical impedance myography signatures in murine spinal cord injury neurogenic bladder models.

Neurogenic bladder (NB) affects people of all ages. Electric impedance myography (EIM) assesses localized muscle abnormalities. Here, we sought to investigate whether unique detrusor EIM signatures are present in NB due to spinal cord injury (SCI). Twenty-eight, 8-10 weeks old, C57BL/6J female mice were studied. Twenty underwent spinal cord transection; 8 served as controls. Cohorts were euthanized at 4 and 6 weeks after spinal cord transection. Each bladder was measured in-situ with EIM with applied frequencies of 1 kHz to 10 MHz, and then processed for molecular and histologic study. SCI mice had greater bladder-to-body weight ratio (p < 0.0001), greater collagen deposition (p = 0.009), and greater smooth-muscle-myosin-heavy-chain isoform A/B ratio (p < 0.0001). Compared with the control group, the SCI group was associated with lower phase, reactance, and resistance values (p < 0.01). Significant correlations (p < 0.001) between bladder-to-body weight ratios and EIM measurements were observed across the entire frequency spectrum. A severely hypertrophied phenotype was characterized by even greater bladder-to-body weight ratios and more depressed EIM values. Our study demonstrated distinct EIM alterations in the detrusor muscle of mice with NB due to SCI. With further refinement, EIM may offer a potential point-of-care tool for the assessment of NB and its response to treatment.

Building the foundation for equitable and inclusive research: Seed grant programs to facilitate development of diverse CBPR community-academic research partnerships.

Introduction: The effectiveness of community-based participatory research (CBPR) partnerships to address health inequities is well documented. CBPR integrates knowledge and perspectives of diverse communities throughout the research process, following principles that emphasize trust, power sharing, co-learning, and mutual benefits. However, institutions and funders seldom provide the time and resources needed for the critical stage of equitable partnership formation and development. Methods: Since 2011, the Detroit Urban Research Center, collaborating with other entities, has promoted the development of new community-academic research partnerships through two grant programs that combine seed funding with capacity building support from community and academic instructors/mentors experienced in CBPR. Process and outcomes were evaluated using mixed methods. Results: From 2011 to 2021, 50 partnerships received grants ranging from $2,500 to $30,000, totaling $605,000. Outcomes included equitable partnership infrastructure and processes, innovative pilot research, translation of findings to interventions and policy change, dissemination to multiple audiences, new proposals and projects, and sustained community-academic research partnerships. All partnerships continued beyond the program; over half secured additional funding. Conclusions: Keys to success included participation as community-academic teams, dedicated time for partnership/relationship development, workshops to develop equity-based skills, relationships, and projects, expert community-academic instructor guidance, and connection to additional resources. Findings demonstrate that small amounts of seed funding for newly forming community-academic partnerships, paired with capacity building support, can provide essential time and resources needed to develop diverse, inclusive, equity-focused CBPR partnerships. Building such support into funding initiatives and through academic institutions can enhance impact and sustainability of translational research toward advancing health equity.

Erratum: Building the foundation for equitable and inclusive research: Seed grant programs to facilitate development of diverse CBPR community-academic research partnerships - CORRIGENDUM.

[This corrects the article DOI: 10.1017/cts.2022.495.].

SpheroScan: A User-Friendly Deep Learning Tool for Spheroid Image Analysis.

Background: In recent years, three-dimensional (3D) spheroid models have become increasingly popular in scientific research as they provide a more physiologically relevant microenvironment that mimics in vivo conditions. The use of 3D spheroid assays has proven to be advantageous as it offers a better understanding of the cellular behavior, drug efficacy, and toxicity as compared to traditional two-dimensional cell culture methods. However, the use of 3D spheroid assays is impeded by the absence of automated and user-friendly tools for spheroid image analysis, which adversely affects the reproducibility and throughput of these assays. Results: To address these issues, we have developed a fully automated, web-based tool called SpheroScan, which uses the deep learning framework called Mask Regions with Convolutional Neural Networks (R-CNN) for image detection and segmentation. To develop a deep learning model that could be applied to spheroid images from a range of experimental conditions, we trained the model using spheroid images captured using IncuCyte Live-Cell Analysis System and a conventional microscope. Performance evaluation of the trained model using validation and test datasets shows promising results. Conclusion: SpheroScan allows for easy analysis of large numbers of images and provides interactive visualization features for a more in-depth understanding of the data. Our tool represents a significant advancement in the analysis of spheroid images and will facilitate the widespread adoption of 3D spheroid models in scientific research. The source code and a detailed tutorial for SpheroScan are available at https://github.com/FunctionalUrology/SpheroScan.

Integrated omics analysis unveils a DNA damage response to neurogenic injury.

Spinal cord injury (SCI) evokes profound bladder dysfunction. Current treatments are limited by a lack of molecular data to inform novel therapeutic avenues. Previously, we showed systemic inosine treatment improved bladder function following SCI in rats. Here, we applied multi-omics analysis to explore molecular alterations in the bladder and their sensitivity to inosine following SCI. Canonical pathways regulated by SCI included those associated with protein synthesis, neuroplasticity, wound healing, and neurotransmitter degradation. Upstream regulator analysis identified MYC as a key regulator, whereas causal network analysis predicted multiple regulators of DNA damage response signaling following injury, including PARP-1. Staining for both DNA damage (γH2AX) and PARP activity (poly-ADP-ribose) markers in the bladder was increased following SCI, and attenuated in inosine-treated tissues. Proteomics analysis suggested that SCI induced changes in protein synthesis-, neuroplasticity-, and oxidative stress-associated pathways, a subset of which were shown in transcriptomics data to be inosine-sensitive. These findings provide novel insights into the molecular landscape of the bladder following SCI, and highlight a potential role for PARP inhibition to treat neurogenic bladder dysfunction.

MLcps: machine learning cumulative performance score for classification problems.

BACKGROUND: Assessing the performance of machine learning (ML) models requires careful consideration of the evaluation metrics used. It is often necessary to utilize multiple metrics to gain a comprehensive understanding of a trained model's performance, as each metric focuses on a specific aspect. However, comparing the scores of these individual metrics for each model to determine the best-performing model can be time-consuming and susceptible to subjective user preferences, potentially introducing bias. RESULTS: We propose the Machine Learning Cumulative Performance Score (MLcps), a novel evaluation metric for classification problems. MLcps integrates several precomputed evaluation metrics into a unified score, enabling a comprehensive assessment of the trained model's strengths and weaknesses. We tested MLcps on 4 publicly available datasets, and the results demonstrate that MLcps provides a holistic evaluation of the model's robustness, ensuring a thorough understanding of its overall performance. CONCLUSIONS: By utilizing MLcps, researchers and practitioners no longer need to individually examine and compare multiple metrics to identify the best-performing models. Instead, they can rely on a single MLcps value to assess the overall performance of their ML models. This streamlined evaluation process saves valuable time and effort, enhancing the efficiency of model evaluation. MLcps is available as a Python package at https://pypi.org/project/MLcps/.

SpheroScan: a user-friendly deep learning tool for spheroid image analysis.

BACKGROUND: In recent years, 3-dimensional (3D) spheroid models have become increasingly popular in scientific research as they provide a more physiologically relevant microenvironment that mimics in vivo conditions. The use of 3D spheroid assays has proven to be advantageous as it offers a better understanding of the cellular behavior, drug efficacy, and toxicity as compared to traditional 2-dimensional cell culture methods. However, the use of 3D spheroid assays is impeded by the absence of automated and user-friendly tools for spheroid image analysis, which adversely affects the reproducibility and throughput of these assays. RESULTS: To address these issues, we have developed a fully automated, web-based tool called SpheroScan, which uses the deep learning framework called Mask Regions with Convolutional Neural Networks (R-CNN) for image detection and segmentation. To develop a deep learning model that could be applied to spheroid images from a range of experimental conditions, we trained the model using spheroid images captured using IncuCyte Live-Cell Analysis System and a conventional microscope. Performance evaluation of the trained model using validation and test datasets shows promising results. CONCLUSION: SpheroScan allows for easy analysis of large numbers of images and provides interactive visualization features for a more in-depth understanding of the data. Our tool represents a significant advancement in the analysis of spheroid images and will facilitate the widespread adoption of 3D spheroid models in scientific research. The source code and a detailed tutorial for SpheroScan are available at https://github.com/FunctionalUrology/SpheroScan.

Latino men who have sex with men and HIV in the rural south-eastern USA: findings from ethnographic in-depth interviews.

A community-based participatory research partnership explored HIV risk and potentially effective intervention characteristics to reduce exposure and transmission among immigrant Latino men who have sex with men living in the rural south-eastern USA. Twenty-one participants enrolled and completed a total of 62 ethnographic in-depth interviews. Mean age was 31 (range 18-48) years and English-language proficiency was limited; 18 participants were from Mexico. Four participants reported having sex with men and women during the past three months; two participants self-identified as male-to-female transgender. Qualitative themes that emerged included a lack of accurate information about HIV and prevention; the influence of social-political contexts to sexual risk; and barriers to healthcare services. We also identified eight characteristics of potentially effective interventions for HIV prevention. Our findings suggest that socio-political contexts must be additional targets of change to reduce and eliminate HIV health disparities experienced by immigrant Latino men who have sex with men.

A Single Cell Dissociation Approach for Molecular Analysis of Urinary Bladder in the Mouse Following Spinal Cord Injury.

We describe the implementation of spinal cord injury in mice to elicit detrusor-sphincter dyssynergia, a functional bladder outlet obstruction, and subsequent bladder wall remodeling. To facilitate assessment of the cellular composition of the bladder wall in non-injured control and spinal cord injured mice, we developed an optimized dissociation protocol that supports high cell viability and enables the detection of discrete subpopulations by flow cytometry. Spinal cord injury is created by complete transection of the thoracic spinal cord. At the time of tissue harvest, the animal is perfused with phosphate-buffered saline under deep anesthesia and bladders are harvested into Tyrode's buffer. Tissues are minced prior to incubation in digestion buffer that has been optimized based on the collagen content of mouse bladder as determined by interrogation of publicly available gene expression databases. Following generation of a single cell suspension, material is analyzed by flow cytometry for assessment of cell viability, cell number and specific subpopulations. We demonstrate that the method yields cell populations with greater than 90% viability, and robust representation of cells of mesenchymal and epithelial origin. This method will enable accurate downstream analysis of discrete cell types in mouse bladder and potentially other organs.