Varun Arvind

Organizational metrics of interchromatin speckle factor domains: integrative classifier for stem cell adhesion & lineage signaling.

Stem cell fates on biomaterials are influenced by the complex confluence of microenvironmental cues emanating from soluble growth factors, cell-to-cell contacts, and biomaterial properties. Cell-microenvironment interactions influence the cell fate by initiating a series of outside-in signaling events that traverse from the focal adhesions to the nucleus via the cytoskeleton and modulate the sub-nuclear protein organization and gene expression. Here, we report a novel imaging-based framework that highlights the spatial organization of sub-nuclear proteins, specifically the splicing factor SC-35 in the nucleoplasm, as an integrative marker to distinguish between minute differences of stem cell lineage pathways in response to stimulatory soluble factors, surface topologies, and microscale topographies. This framework involves the high resolution image acquisition of SC-35 domains and imaging-based feature extraction to obtain quantitative nuclear metrics in tandem with machine learning approaches to generate a predictive cell state classification model. The acquired SC-35 metrics led to >90% correct classification of emergent human mesenchymal stem cell (hMSC) phenotypes in populations of hMSCs exposed for merely 3 days to basal, adipogenic, or osteogenic soluble cues, as well as varying levels of dexamethasone-induced alkaline phosphatase (ALP) expression. Early osteogenic cellular responses across a series of surface patterns, fibrous scaffolds, and micropillars were also detected and classified using this imaging-based methodology. Complex cell states resulting from inhibition of RhoGTPase, β-catenin, and FAK could be classified with >90% sensitivity on the basis of differences in the SC-35 organizational metrics. This indicates that SC-35 organization is sensitively impacted by adhesion-related signaling molecules that regulate osteogenic differentiation. Our results show that diverse microenvironment cues affect different attributes of the SC-35 organizational metrics and lead to distinct emergent organizational patterns. Taken together, these studies demonstrate that the early organization of SC-35 domains could serve as a fingerprint of the intracellular mechanotransductive signaling that governs growth factor- and topography-responsive stem cell states.

High-content image informatics of the structural nuclear protein NuMA parses trajectories for stem/progenitor cell lineages and oncogenic transformation

ABSTRACT Stem and progenitor cells that exhibit significant regenerative potential and critical roles in cancer initiation and progression remain difficult to characterize. Cell fates are determined by reciprocal signaling between the cell microenvironment and the nucleus; hence parameters derived from nuclear remodeling are ideal candidates for stem/progenitor cell characterization. Here we applied high‐content, single cell analysis of nuclear shape and organization to examine stem and progenitor cells destined to distinct differentiation endpoints, yet undistinguishable by conventional methods. Nuclear descriptors defined through image informatics classified mesenchymal stem cells poised to either adipogenic or osteogenic differentiation, and oligodendrocyte precursors isolated from different regions of the brain and destined to distinct astrocyte subtypes. Nuclear descriptors also revealed early changes in stem cells after chemical oncogenesis, allowing the identification of a class of cancer‐mitigating biomaterials. To capture the metrology of nuclear changes, we developed a simple and quantitative “imaging‐derived” parsing index, which reflects the dynamic evolution of the high‐dimensional space of nuclear organizational features. A comparative analysis of parsing outcomes via either nuclear shape or textural metrics of the nuclear structural protein NuMA indicates the nuclear shape alone is a weak phenotypic predictor. In contrast, variations in the NuMA organization parsed emergent cell phenotypes and discerned emergent stages of stem cell transformation, supporting a prognosticating role for this protein in the outcomes of nuclear functions. HIGHLIGHTSHigh‐content analysis of nuclear shape and organization classify stem and progenitor cells poised for distinct lineages.Early oncogenic changes in mesenchymal stem cells (MSCs) are also detected with nuclear descriptors.A new class of cancer‐mitigating biomaterials was identified based on image informatics.Textural metrics of the nuclear structural protein NuMA are sufficient to parse emergent cell phenotypes.

Mechanobiology of limb musculoskeletal development

While there has been considerable progress in identifying molecular regulators of musculoskeletal development, the role of physical forces in regulating induction, differentiation, and patterning events is less well understood. Here, we highlight recent findings in this area, focusing primarily on model systems that test the mechanical regulation of skeletal and tendon development in the limb. We also discuss a few of the key signaling pathways and mechanisms that have been implicated in mechanotransduction and highlight current gaps in knowledge and opportunities for further research in the field.

Predicting Surgical Complications in Patients Undergoing Elective Adult Spinal Deformity Procedures Using Machine Learning

STUDY DESIGNnCross-sectional database study.nnnOBJECTIVEnTo train and validate machine learning models to identify risk factors for complications following surgery for adult spinal deformity (ASD).nnnSUMMARY OF BACKGROUND DATAnMachine learning models such as logistic regression (LR) and artificial neural networks (ANNs) are valuable tools for analyzing and interpreting large and complex data sets. ANNs have yet to be used for risk factor analysis in orthopedic surgery.nnnMETHODSnThe American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database was queried for patients who underwent surgery for ASD. This query returned 4,073 patients, which data were used to train and evaluate our models. The predictive variables used included sex, age, ethnicity, diabetes, smoking, steroid use, coagulopathy, functional status, American Society of Anesthesiologists (ASA) class >3, body mass index (BMI), pulmonary comorbidities, and cardiac comorbidities. The models were used to predict cardiac complications, wound complications, venous thromboembolism (VTE), and mortality. Using ASA class as a benchmark for prediction, area under receiver operating characteristic curves (AUC) was used to determine the accuracy of our machine learning models.nnnRESULTSnThe mean age of patients was 59.5 years. Forty-one percent of patients were male whereas 59.0% of patients were female. ANN and LR outperformed ASA scoring in predicting every complication (p<.05). The ANN outperformed LR in predicting cardiac complication, wound complication, and mortality (p<.05).nnnCONCLUSIONSnMachine learning algorithms outperform ASA scoring for predicting individual risk prognosis. These algorithms also outperform LR in predicting individual risk for all complications except VTE. With the growing size of medical data, the training of machine learning on these large data sets promises to improve risk prognostication, with the ability of continuously learning making them excellent tools in complex clinical scenarios.nnnLEVEL OF EVIDENCEnLevel III.STUDY DESIGNnCross-sectional database study.nnnOBJECTIVEnTo train and validate machine learning models to identify risk factors for complications following surgery for adult spinal deformity (ASD). Machine learning models such as logistic regression (LR) and artificial neural networks (ANNs) are valuable tools for analyzing and interpreting large and complex data sets. ANNs have yet to be used for risk factor analysis in orthopedic surgery.nnnMETHODSnThe American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database was queried for patients who underwent surgery for ASD. This query returned 4,073 patients, which data were used to train and evaluate our models. The predictive variables used included sex, age, ethnicity, diabetes, smoking, steroid use, coagulopathy, functional status, American Society of Anesthesiologists (ASA) class >3, body mass index (BMI), pulmonary comorbidities, and cardiac comorbidities. The models were used to predict cardiac complications, wound complications, venous thromboembolism (VTE), and mortality. Using ASA class as a benchmark for prediction, area under receiver operating characteristic curves (AUC) was used to determine the accuracy of our machine learning models.nnnRESULTSnThe mean age of patients was 59.5 years. Forty-one percent of patients were male whereas 59.0% of patients were female. ANN and LR outperformed ASA scoring in predicting every complication (p<.05). The ANN outperformed LR in predicting cardiac complication, wound complication, and mortality (p<.05).nnnCONCLUSIONSnMachine learning algorithms outperform ASA scoring for predicting individual risk prognosis. These algorithms also outperform LR in predicting individual risk for all complications except VTE. With the growing size of medical data, the training of machine learning on these large data sets promises to improve risk prognostication, with the ability of continuously learning making them excellent tools in complex clinical scenarios.nnnLEVEL OF EVIDENCEnLevel III.

Primary Versus Revision Discectomy for Adults With Herniated Nucleus Pulposus: A Propensity Score–Matched Multicenter Study

Study Design: Retrospective propensity score matched analysis. Objective: To compare the incidence of any 30-day perioperative complication following primary and revision discectomy for lumbar disc herniation. Methods: The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) was used to identify patients undergoing primary or revision lumbar discectomy from 2005 to 2012. Propensity score matching was performed to create matched pairs of primary and revision discectomy cases for analysis. Univariate analysis was then performed to compare 30-day morbidity and mortality between propensity score–matched pairs. Results: We identified 4730 cases of primary discectomy performed through a minimally invasive or open approach and 649 revision discectomy cases. Baseline patient characteristics and comorbidities were compared and then propensity score–matched adjustments were made to create 649 matched pairs of primary and revision cases. On univariate analysis, there were no significant differences in 30-day perioperative outcomes between the 2 groups. Conclusion: While there were no significant differences in 30-day perioperative complications between patients undergoing primary lumbar discectomy and those undergoing revision lumbar discectomy, this finding should be interpreted with caution since the ACS-NSQIP database lacks functional and pain outcomes, and also does not include dural tear or durotomy as a complication. Future large-scale and long-term prospective studies including these variables are needed to better understand the outcomes and complications following primary versus revision discectomy for lumbar disc herniation.

Hypoalbuminemia as an Independent Risk Factor for Perioperative Complications Following Surgical Decompression of Spinal Metastases

Study Design: Retrospective cohort study. Objective: Malnutrition has been shown to be a risk factor for poor perioperative outcomes in multiple surgical subspecialties, but few studies have specifically investigated the effect of hypoalbuminemia in patients undergoing operative treatment of metastatic spinal tumors. The aim of this study was to assess the role of hypoalbuminemia as an independent risk factor for 30-day perioperative mortality and morbidity after surgical decompression of metastatic spinal tumors using the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database from 2011 to 2014. Methods: We identified 1498 adult patients in the ACS-NSQIP database who underwent laminectomy and excision of metastatic extradural spinal tumors. Patients were categorized into normoalbuminemic and hypoalbuminemic (ie, albumin level <3.5 g/dL) groups. Univariate and multivariate regression analyses were performed to examine the association between preoperative hypoalbuminemia and 30-day perioperative mortality and morbidity. Subgroup analysis was performed in the hypoalbuminemic group to assess the dose-dependent effect of albumin depletion. Results: Hypoalbuminemia was associated with increased risk of perioperative mortality, any complication, sepsis, intra- or postoperative transfusion, prolonged hospitalization, and non-home discharge. However, albumin depletion was also associated with decreased risk of readmission. There was an albumin level–dependent effect of increasing mortality and complication rates with worsening albumin depletion. Conclusions: Hypoalbuminemia is an independent risk factor for perioperative mortality and morbidity following surgical decompression of metastatic spinal tumors with a dose-dependent effect on mortality and complication rates. Therefore, it is important to address malnutrition and optimize nutritional status prior to surgery.

Surgical, Radiographic, and Patient-Related Risk Factors for Proximal Junctional Kyphosis: A Meta-Analysis

Study Design: Meta-analysis. Objective: Proximal junctional kyphosis (PJK) is a complication of surgical management for adult spinal deformity with a multifactorial etiology. Many risk factors are controversial and their relative importance are not fully understood. We aimed to identify the surgical, radiographic, and patient-related risk factors associated with PJK and proximal junctional failure (PJF). Methods: A systematic literature search was performed using PubMed, Cochrane Database of Systematic Reviews, and EMBASE. The inclusion criteria included prospective randomized control trials and prospective/retrospective cohort studies of adult patients with radiographic evidence of PJK, which was defined as a proximal junctional sagittal Cobb angle ≥10° and at least 10° greater than the preoperative measurement. Studies required a minimum of 10 patients and 12 months of follow-up. Results: A total of 14 unique studies, including 1908 patients were included. The pooled analysis showed significant differences between the PJK and non-PJK groups in age (weighted mean difference [WMD] −3.80; P = .03), prevalence of osteopenia/osteoporosis (odds ratio [OR] 1.99; P = .0004), preoperative sagittal vertical axis (SVA) (WMD −17.52; P = .02), preoperative lumbar lordosis (LL) (WMD −1.22; P = .002), pedicle screw instrumentation at the upper instrumented vertebra (UIV) (OR 1.67; P = .02), change in SVA (WMD −11.87; P = .01), fusion to sacrum/pelvis/ilium (OR 2.14; P < .00u2009001), change in LL (WMD −5.61; P = .01), and postoperative SVA (WMD −7.79; P = .008). Conclusions: Our meta-analysis suggests that age, osteopenia/osteoporosis, high preoperative SVA, high postoperative SVA, low preoperative LL, use of pedicle screws at the UIV, SVA change/correction, LL change/correction, and fusion to sacrum/pelvis/iliac region are risk factors for PJK.

Role of Posterior Ligamentous Reinforcement in Proximal Junctional Kyphosis: A Cadaveric Biomechanical Study

Study Design Cadaveric biomechanical study. Purpose The purpose of this study was to biomechanically evaluate the effect of preserving or augmenting the interspinous ligament (ISL) and supraspinous ligament (SSL; ISL/SSL) complex between the upper instrumented vertebra (UIV) and UIV+1 using a cadaveric model. Overview of Literature Adult spinal deformity is becoming an increasingly prevalent disorder, and proximal junctional kyphosis (PJK) is a well-known postoperative complication following long spinal fusion. Methods Pure moments of 4 and 8 Nm were applied to the native and instrumented spine, respectively (n=8). The test conditions included the following: native spine (T7–L2), fused spine (T10–L2), fused spine with a hand-tied suture loop through the spinous processes at T9–T10, and fused spine with severed T9–T10 ISL/SSL complex. Results The flexion range of motion (ROM) at T9–T10 of the fused spine loaded at 8 Nm increased by 62% compared to that of the native spine loaded at 4 Nm. The average flexion ROM at T9–T10 for the suture loop and severed ISL/SSL spines were 141% (p=0.13) and 177% (p=0.66) of the native spine at 4 Nm, respectively (p-values vs. fused). Conclusions Transection of the ISL/SSL complex did not significantly change flexion ROM at the proximal junctional segment following instrumented spinal fusion. Furthermore, augmentation of the posterior ligamentous tension band with a polyester fiber suture loop did not mitigate excessive flexion loads on the proximal junctional segment. We postulate that the role of the posterior ligamentous tension band in mitigating PJK is secondary to the anterior column support provided by the vertebral body and intervertebral disc.

Substrate micropatterns produced by polymer demixing regulate focal adhesions, actin anisotropy, and lineage differentiation of stem cells

Stem cells are adherent cells whose multipotency and differentiation can be regulated by numerous microenvironmental signals including soluble growth factors and surface topography. This study describes a simple method for creating distinct micropatterns via microphase separation resulting from polymer demixing of poly(desaminotyrosyl-tyrosine carbonate) (PDTEC) and polystyrene (PS). Substrates with co-continuous (ribbons) or discontinuous (islands and pits) PDTEC regions were obtained by varying the ratio of PDTEC and sacrificial PS. Human mesenchymal stem cells (MSCs) cultured on co-continuous PDTEC substrates for 3 days in bipotential adipogenic/osteogenic (AD/OS) induction medium showed no change in cell morphology but exhibited increased anisotropic cytoskeletal organization and larger focal adhesions when compared to MSCs cultured on discontinuous micropatterns. After 14 days in bipotential AD/OS induction medium, MSCs cultured on co-continuous micropatterns exhibited increased expression of osteogenic markers, whereas MSCs on discontinuous PDTEC substrates showed a low expression of adipogenic and osteogenic differentiation markers. Substrates with graded micropatterns were able to reproduce the influence of local underlying topography on MSC differentiation, thus demonstrating their potential for high throughput analysis. This work presents polymer demixing as a simple, non-lithographic technique to produce a wide range of micropatterns on surfaces with complex geometries to influence cellular and tissue regenerative responses.nnnSTATEMENT OF SIGNIFICANCEnA better understanding of how engineered microenvironments influence stem cell differentiation is integral to increasing the use of stem cells and materials in a wide range of tissue engineering applications. In this study, we show the range of topography obtained by polymer demixing is sufficient for investigating how surface topography affects stem cell morphology and differentiation. Our findings show that co-continuous topographies favor early (3-day) cytoskeletal anisotropy and focal adhesion maturation as well as long-term (14-day) expression of osteogenic differentiation markers. Taken together, this study presents a simple approach to pattern topographies that induce divergent responses in stem cell morphology and differentiation.