More Similar than Different: Memory, Executive Functions, Cortical Thickness, and Glucose Metabolism in Biomarker-Positive Alzheimer's Disease and Behavioral Variant Frontotemporal Dementia.

Background: Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD) are typically associated with very different clinical and neuroanatomical presentations; however, there is increasing recognition of similarities. Objective: To examine memory and executive functions, as well as cortical thickness, and glucose metabolism in AD and bvFTD signature brain regions. Methods: We compared differences in a group of biomarker-defined participants with Alzheimer's disease and a group of clinically diagnosed participants with bvFTD. These groups were also contrasted with healthy controls (HC). Results: As expected, memory functions were generally more impaired in AD, followed by bvFTD, and both clinical groups performed more poorly than the HC group. Executive function measures were similar in AD compared to bvFTD for motor sequencing and go/no-go, but bvFTD had more difficulty with a set shifting task. Participants with AD showed thinner cortex and lower glucose metabolism in the angular gyrus compared to bvFTD. Participants with bvFTD had thinner cortex in the insula and temporal pole relative to AD and healthy controls, but otherwise the two clinical groups were similar for other frontal and temporal signature regions. Conclusions: Overall, the results of this study highlight more similarities than differences between AD and bvFTD in terms of cognitive functions, cortical thickness, and glucose metabolism. Further research is needed to better understand the mechanisms mediating this overlap and how these relationships evolve longitudinally.

Developing, implementing, and evaluating the visiting Neighbors' program in rural Appalachia: A quality improvement protocol.

INTRODUCTION: Older adults living alone in rural areas frequently experience health declines, social isolation, and limited access to services. To address these challenges, our medical academic university supported a quality improvement project for developing and evaluating the Visiting Neighbors program in two rural Appalachian counties. Our Visiting Neighbors program trained local volunteers to visit and guide rural older adults in healthy activities. These age-appropriate activities (Mingle, Manage, and Move- 3M's) were designed to improve the functional health of older adults. The program includes four in-home visits and four follow-up telephone calls across three months. PURPOSE: The purpose of this paper was to describe the 3M's Visiting Neighbors protocol steps guiding the quality improvement procedures relating to program development, implementation, and evaluation. METHODS AND MATERIALS: This Visiting Neighbors study used a single-group exploratory quality improvement design. This program was tested using quality improvement standards, including collecting participant questionnaires and visit observations. RESULTS: Older adults (> 65 years) living alone (N = 30) participants were female (79%) with a mean age of 82.96 (SD = 7.87) years. Volunteer visitor participants (N = 10) were older adult females. Two volunteer visitors implemented each visit, guided by the 3M's activities manual. All visits were verified as being consistently delivered (fidelity). Enrollment and retention data found the program was feasible to conduct. The older adult participants' total program helpfulness ratings (1 to 5) were high (M = 51.27, SD = 3.77). All volunteer visitor's program helpfulness ratings were also high (M = 51.78, SD = 3.73). DISCUSSION: The Visiting Neighbors program consistently engaged older Appalachian adults living alone in the 3M's activities. The feasibility and fidelity of the 3M's home visits were verified. The quality improvement processes included engaging the expert advisory committee and rural county stakeholders to ensure the quality of the program development, implementation, and evaluation.

Ultrasound Blood-Brain Barrier Opening and Aducanumab in Alzheimer's Disease.

Antiamyloid antibodies have been used to reduce cerebral amyloid-beta (Aß) load in patients with Alzheimer's disease. We applied focused ultrasound with each of six monthly aducanumab infusions to temporarily open the blood-brain barrier with the goal of enhancing amyloid removal in selected brain regions in three participants over a period of 6 months. The reduction in the level of Aß was numerically greater in regions treated with focused ultrasound than in the homologous regions in the contralateral hemisphere that were not treated with focused ultrasound, as measured by fluorine-18 florbetaben positron-emission tomography. Cognitive tests and safety evaluations were conducted over a period of 30 to 180 days after treatment. (Funded by the Harry T. Mangurian, Jr. Foundation and the West Virginia University Rockefeller Neuroscience Institute.).

Surface-based correlates of cognition along the Alzheimer's continuum in a memory clinic population.

Composite cognitive measures in large-scale studies with biomarker data for amyloid and tau have been widely used to characterize Alzheimer's disease (AD). However, little is known about how the findings from these studies translate to memory clinic populations without biomarker data, using single measures of cognition. Additionally, most studies have utilized voxel-based morphometry or limited surface-based morphometry such as cortical thickness, to measure the neurodegeneration associated with cognitive deficits. In this study, we aimed to replicate and extend the biomarker, composite study relationships using expanded surface-based morphometry and single measures of cognition in a memory clinic population. We examined 271 clinically diagnosed symptomatic individuals with mild cognitive impairment (N = 93) and Alzheimer's disease dementia (N = 178), as well as healthy controls (N = 29). Surface-based morphometry measures included cortical thickness, sulcal depth, and gyrification index within the "signature areas" of Alzheimer's disease. The cognitive variables pertained to hallmark features of Alzheimer's disease including verbal learning, verbal memory retention, and language, as well as executive function. The results demonstrated that verbal learning, language, and executive function correlated with the cortical thickness of the temporal, frontal, and parietal areas. Verbal memory retention was correlated to the thickness of temporal regions and gyrification of the inferior temporal gyrus. Language was related to the temporal regions and the supramarginal gyrus' sulcal depth and gyrification index. Executive function was correlated with the medial temporal gyrus and supramarginal gyrus sulcal depth, and the gyrification index of temporal regions and supramarginal gyrus, but not with the frontal areas. Predictions of each of these cognitive measures were dependent on a combination of structures and each of the morphometry measurements, and often included medial temporal gyrus thickness and sulcal depth. Overall, the results demonstrated that the relationships between cortical thinning and cognition are widespread and can be observed using single measures of cognition in a clinically diagnosed AD population. The utility of sulcal depth and gyrification index measures may be more focal to certain brain areas and cognitive measures. The relative importance of temporal, frontal, and parietal regions in verbal learning, language, and executive function, but not verbal memory retention, was replicated in this clinic cohort.

Pleiotropic loci for cannabis use disorder severity in multi-ancestry high-risk populations.

Cannabis use disorder (CUD) is common and has in part a genetic basis. The risk factors underlying its development likely involve multiple genes that are polygenetic and interact with each other and the environment to ultimately lead to the disorder. Co-morbidity and genetic correlations have been identified between CUD and other disorders and traits in select populations primarily of European descent. If two or more traits, such as CUD and another disorder, are affected by the same genetic locus, they are said to be pleiotropic. The present study aimed to identify specific pleiotropic loci for the severity level of CUD in three high-risk population cohorts: American Indians (AI), Mexican Americans (MA), and European Americans (EA). Using a previously developed computational method based on a machine learning technique, we leveraged the entire GWAS catalog and identified 114, 119, and 165 potentially pleiotropic variants for CUD severity in AI, MA, and EA respectively. Ten pleiotropic loci were shared between the cohorts although the exact variants from each cohort differed. While majority of the pleiotropic genes were distinct in each cohort, they converged on numerous enriched biological pathways. The gene ontology terms associated with the pleiotropic genes were predominately related to synaptic functions and neurodevelopment. Notable pathways included Wnt/ß-catenin signaling, lipoprotein assembly, response to UV radiation, and components of the complement system. The pleiotropic genes were the most significantly differentially expressed in frontal cortex and coronary artery, up-regulated in adipose tissue, and down-regulated in testis, prostate, and ovary. They were significantly up-regulated in most brain tissues but were down-regulated in the cerebellum and hypothalamus. Our study is the first to attempt a large-scale pleiotropy detection scan for CUD severity. Our findings suggest that the different population cohorts may have distinct genetic factors for CUD, however they share pleiotropic genes from underlying pathways related to Alzheimer's disease, neuroplasticity, immune response, and reproductive endocrine systems.

Procedural learning and retention relative to explicit learning and retention in mild cognitive impairment and Alzheimer's disease using a modification of the trail making test.

Amnestic mild cognitive impairment (aMCI) and Alzheimer's disease (AD) dementia are characterized by pathological changes to the medial temporal lobes, resulting in explicit learning and retention reductions. Studies demonstrate that implicit/procedural memory processes are relatively intact in these populations, supporting different anatomical substrates for differing memory systems. This study examined differences between explicit and procedural learning and retention in individuals with aMCI and AD dementia relative to matched healthy controls. We also examined anatomical substrates using volumetric MRI. Results revealed expected difficulties with explicit learning and retention in individuals with aMCI and AD with relatively preserved procedural memory. Explicit verbal retention was associated with medial temporal cortex volumes. However, procedural retention was not related to medial temporal or basal ganglia volumes. Overall, this study confirms the dissociation between explicit relative to procedural learning and retention in aMCI and AD dementia and supports differing anatomical substrates.

Focused ultrasound-mediated blood-brain barrier opening in Alzheimer's disease: long-term safety, imaging, and cognitive outcomes.

OBJECTIVE: MRI-guided low-intensity focused ultrasound (FUS) has been shown to reversibly open the blood-brain barrier (BBB), with the potential to deliver therapeutic agents noninvasively to target brain regions in patients with Alzheimer's disease (AD) and other neurodegenerative conditions. Previously, the authors reported the short-term safety and feasibility of FUS BBB opening of the hippocampus and entorhinal cortex (EC) in patients with AD. Given the need to treat larger brain regions beyond the hippocampus and EC, brain volumes and locations treated with FUS have now expanded. To evaluate any potential adverse consequences of BBB opening on disease progression, the authors report safety, imaging, and clinical outcomes among participants with mild AD at 6-12 months after FUS treatment targeted to the hippocampus, frontal lobe, and parietal lobe. METHODS: In this open-label trial, participants with mild AD underwent MRI-guided FUS sonication to open the BBB in ß-amyloid positive regions of the hippocampus, EC, frontal lobe, and parietal lobe. Participants underwent 3 separate FUS treatment sessions performed 2 weeks apart. Outcome assessments included safety, imaging, neurological, cognitive, and florbetaben ß-amyloid PET. RESULTS: Ten participants (range 55-76 years old) completed 30 separate FUS treatments at 2 participating institutions, with 6-12 months of follow-up. All participants had immediate BBB opening after FUS and BBB closure within 24-48 hours. All FUS treatments were well tolerated, with no serious adverse events related to the procedure. All 10 participants had a minimum of 6 months of follow-up, and 7 participants had a follow-up out to 1 year. Changes in the Alzheimer's Disease Assessment Scale-cognitive and Mini-Mental State Examination scores were comparable to those in controls from the Alzheimer's Disease Neuroimaging Initiative. PET scans demonstrated an average ß-amyloid plaque of 14% in the Centiloid scale in the FUS-treated regions. CONCLUSIONS: This study is the largest cohort of participants with mild AD who received FUS treatment, and has the longest follow-up to date. Safety was demonstrated in conjunction with reversible and repeated BBB opening in multiple cortical and deep brain locations, with a concomitant reduction of ß-amyloid. There was no apparent cognitive worsening beyond expectations up to 1 year after FUS treatment, suggesting that the BBB opening treatment in multiple brain regions did not adversely influence AD progression. Further studies are needed to determine the clinical significance of these findings. FUS offers a unique opportunity to decrease amyloid plaque burden as well as the potential to deliver targeted therapeutics to multiple brain regions in patients with neurodegenerative disorders.

Frontal and temporal lobe correlates of verbal learning and memory in aMCI and suspected Alzheimer's disease dementia.

Alzheimer's disease is primarily known for deficits in learning and retaining new information. This has long been associated with pathological changes in the mesial temporal lobes. The role of the frontal lobes in memory in Alzheimer's disease is less well understood. In this study, we examined the role of the frontal lobes in learning, recognition, and retention of new verbal information, as well as the presence of specific errors (i.e., intrusions and false-positive errors). Participants included one hundred sixty-seven patients clinically diagnosed with amnestic mild cognitive impairment or suspected Alzheimer's disease dementia who were administered the California Verbal Learning Test and completed high-resolution MRI. We confirmed the role of the mesial temporal lobes in learning and retention, including the volumes of the hippocampus, entorhinal cortex, and parahippocampal gyrus. In addition, false-positive errors were associated with all volumes of the mesial temporal lobes and widespread areas within the frontal lobes. Errors of intrusion were related to the supplementary motor cortex and hippocampus. Most importantly, the mesial temporal lobes interacted with the frontal lobes for learning, recognition, and memory errors. Lower volumes in both regions explained more performance variance than any single structure. This study supports the interaction of the frontal lobes with the temporal lobes in many aspects of memory in Alzheimer's disease.

Association of Predicted Expression and Multimodel Association Analysis of Substance Abuse Traits.

Introduction: Genome-wide association studies (GWAS) have played a critical role in identifying many thousands of loci associated with complex phenotypes and diseases. This has led to several translations of novel disease susceptibility genes into drug targets and care. This however has not been the case for analyses where sample sizes are small, which suffer from multiple comparisons testing. The present study examined the statistical impact of combining a burden test methodology, PrediXcan, with a multimodel meta-analysis, cross phenotype association (CPASSOC). Methods: The analysis was conducted on 5 addiction traits: family alcoholism, cannabis craving, alcohol, nicotine, and cannabis dependence and 10 brain tissues: anterior cingulate cortex BA24, cerebellar hemisphere, cortex, hippocampus, nucleus accumbens basal ganglia, caudate basal ganglia, cerebellum, frontal cortex BA9, hypothalamus, and putamen basal ganglia. Our sample consisted of 1,640 participants from the University of California, San Francisco (UCSF) Family Alcoholism Study. Genotypes were obtained through low pass whole genome sequencing and the use of Thunder, a linkage disequilibrium variant caller. Results: The post-PrediXcan, gene-phenotype association without aggregation resulted in 2 significant results, HCG27 and SPPL2B. Aggregating across phenotypes resulted no significant findings. Aggregating across tissues resulted in 15 significant and 5 suggestive associations: PPIE, RPL36AL, FOXN2, MTERF4, SEPTIN2, CIAO3, RPL36AL, ZNF304, CCDC66, SSPOP, SLC7A9, LY75, MTRF1L, COA5, and RRP7A; RPS23, GNMT, ERV3-1, APIP, and HLA-B, respectively. Discussion: Given the relatively small size of the cohort, this multimodel approach was able to find over a dozen significant associations between predicted gene expression and addiction traits. Of our findings, 8 had prior associations with similar phenotypes through investigation of the GWAS Atlas. With the onset of improved transcriptome data, this approach should increase in efficacy.

A greedy regression algorithm with coarse weights offers novel advantages.

Regularized regression analysis is a mature analytic approach to identify weighted sums of variables predicting outcomes. We present a novel Coarse Approximation Linear Function (CALF) to frugally select important predictors and build simple but powerful predictive models. CALF is a linear regression strategy applied to normalized data that uses nonzero weights + 1 or - 1. Qualitative (linearly invariant) metrics to be optimized can be (for binary response) Welch (Student) t-test p-value or area under curve (AUC) of receiver operating characteristic, or (for real response) Pearson correlation. Predictor weighting is critically important when developing risk prediction models. While counterintuitive, it is a fact that qualitative metrics can favor CALF with ± 1 weights over algorithms producing real number weights. Moreover, while regression methods may be expected to change most or all weight values upon even small changes in input data (e.g., discarding a single subject of hundreds) CALF weights generally do not so change. Similarly, some regression methods applied to collinear or nearly collinear variables yield unpredictable magnitude or the direction (in p-space) of the weights as a vector. In contrast, with CALF if some predictors are linearly dependent or nearly so, CALF simply chooses at most one (the most informative, if any) and ignores the others, thus avoiding the inclusion of two or more collinear variables in the model.

TWO-SIGMA-G: a new competitive gene set testing framework for scRNA-seq data accounting for inter-gene and cell-cell correlation.

We propose TWO-SIGMA-G, a competitive gene set test for scRNA-seq data. TWO-SIGMA-G uses a mixed-effects regression model based on our previously published TWO-SIGMA to test for differential expression at the gene-level. This regression-based model provides flexibility and rigor at the gene-level in (1) handling complex experimental designs, (2) accounting for the correlation between biological replicates and (3) accommodating the distribution of scRNA-seq data to improve statistical inference. Moreover, TWO-SIGMA-G uses a novel approach to adjust for inter-gene-correlation (IGC) at the set-level to control the set-level false positive rate. Simulations demonstrate that TWO-SIGMA-G preserves type-I error and increases power in the presence of IGC compared with other methods. Application to two datasets identified HIV-associated interferon pathways in xenograft mice and pathways associated with Alzheimer's disease progression in humans.

Effects of genetic risk for alcohol dependence and onset of regular drinking on the progression to alcohol dependence: A polygenic risk score approach.

BACKGROUND: Prior studies have established the importance of genetic contributions to the etiology of alcohol dependence (AD), and suggested an early onset of alcohol use represents an initial marker of this genetic risk, which is associated with a more rapid progression to AD and increased risk for AD itself. Building on prior work, the current study examined whether the additive effects of AD risk variants predicted the rate of progression to AD from the onset of regular drinking, a drinking milestone with high clinical relevance to AD prevention. METHODS: Data from 1501 European-ancestry adults from the University of California - San Francisco Family Alcoholism Study were used to examine whether polygenic risk scores for AD (PRSAD) and age-at-onset of regular drinking contributed uniquely to the likelihood of having a lifetime AD diagnosis and the rate of progression from regular drinking to AD. Mixed effects logistic regression and Cox proportional hazards regression analyses were employed. RESULTS: Increases in PRSAD were associated with a faster progression from regular drinking to AD independent of age-at-onset of regular drinking. An independent effect of age-at-onset of regular drinking was also observed indicating that a one-year delay in regular drinking was associated with a 7% decrease in the hazard of progression to AD among drinkers with an early onset (≤ 18), but a 3% increase among drinkers with a late onset (> 18) of regular drinking. CONCLUSIONS: These results broaden our understanding of the contributions of measured genotypes underlying AD-risk on the etiology and clinical course of AD.

Noninvasive prenatal exome sequencing diagnostic utility limited by sequencing depth and fetal fraction.

OBJECTIVE: Sequencing cell-free DNA now allows detection of large chromosomal abnormalities and dominant Mendelian disorders in the prenatal period. Improving upon these methods would allow newborn screening programs to begin with prenatal genetics, ultimately improving the management of rare genetic disorders. METHODS: As a pilot study, we performed exome sequencing on the cell-free DNA from three mothers with singleton pregnancies to assess the viability of broad sequencing modalities in a noninvasive prenatal setting. RESULTS: We found poor resolution of maternal and fetal genotypes due to both sampling and technical issues. CONCLUSION: We find broad sequencing modalities inefficient for noninvasive prenatal applications. Alternatively, we suggest a more targeted path forward for noninvasive prenatal genotyping.

Pre-capture multiplexing provides additional power to detect copy number variation in exome sequencing.

BACKGROUND: As exome sequencing (ES) integrates into clinical practice, we should make every effort to utilize all information generated. Copy-number variation can lead to Mendelian disorders, but small copy-number variants (CNVs) often get overlooked or obscured by under-powered data collection. Many groups have developed methodology for detecting CNVs from ES, but existing methods often perform poorly for small CNVs and rely on large numbers of samples not always available to clinical laboratories. Furthermore, methods often rely on Bayesian approaches requiring user-defined priors in the setting of insufficient prior knowledge. This report first demonstrates the benefit of multiplexed exome capture (pooling samples prior to capture), then presents a novel detection algorithm, mcCNV ("multiplexed capture CNV"), built around multiplexed capture. RESULTS: We demonstrate: (1) multiplexed capture reduces inter-sample variance; (2) our mcCNV method, a novel depth-based algorithm for detecting CNVs from multiplexed capture ES data, improves the detection of small CNVs. We contrast our novel approach, agnostic to prior information, with the the commonly-used ExomeDepth. In a simulation study mcCNV demonstrated a favorable false discovery rate (FDR). When compared to calls made from matched genome sequencing, we find the mcCNV algorithm performs comparably to ExomeDepth. CONCLUSION: Implementing multiplexed capture increases power to detect single-exon CNVs. The novel mcCNV algorithm may provide a more favorable FDR than ExomeDepth. The greatest benefits of our approach derive from (1) not requiring a database of reference samples and (2) not requiring prior information about the prevalance or size of variants.

Cryptic developmental events determine medulloblastoma radiosensitivity and cellular heterogeneity without altering transcriptomic profile.

It is unclear why medulloblastoma patients receiving similar treatments experience different outcomes. Transcriptomic profiling identified subgroups with different prognoses, but in each subgroup, individuals remain at risk of incurable recurrence. To investigate why similar-appearing tumors produce variable outcomes, we analyzed medulloblastomas triggered in transgenic mice by a common driver mutation expressed at different points in brain development. We genetically engineered mice to express oncogenic SmoM2, starting in multipotent glio-neuronal stem cells, or committed neural progenitors. Both groups developed medulloblastomas with similar transcriptomic profiles. We compared medulloblastoma progression, radiosensitivity, and cellular heterogeneity, determined by single-cell transcriptomic analysis (scRNA-seq). Stem cell-triggered medulloblastomas progressed faster, contained more OLIG2-expressing stem-like cells, and consistently showed radioresistance. In contrast, progenitor-triggered MBs progressed slower, down-regulated stem-like cells and were curable with radiation. Progenitor-triggered medulloblastomas also contained more diverse stromal populations, with more Ccr2+ macrophages and fewer Igf1+ microglia, indicating that developmental events affected the subsequent tumor microenvironment. Reduced mTORC1 activity in M-Smo tumors suggests that differential Igf1 contributed to differences in phenotype. Developmental events in tumorigenesis that were obscure in transcriptomic profiles thus remained cryptic determinants of tumor composition and outcome. Precise understanding of medulloblastoma pathogenesis and prognosis requires supplementing transcriptomic/methylomic studies with analyses that resolve cellular heterogeneity.

Common genetic substrates of alcohol and substance use disorder severity revealed by pleiotropy detection against GWAS catalog in two populations.

Alcohol and other substance use disorders (AUD and SUD) are complex diseases that are postulated to have a polygenic inheritance and are often comorbid with other disorders. The comorbidities may arise partially through genetic pleiotropy. Identification of specific gene variants accounting for large parts of the variance in these disorders has yet to be accomplished. We describe a flexible strategy that takes a variant-trait association database and determines if a subset of disease/straits are potentially pleiotropic with the disorder under study. We demonstrate its usage in a study of use disorders in two independent cohorts: alcohol, stimulants, cannabis (CUD), and multi-substance use disorders (MSUD) in American Indians (AI) and AUD and CUD in Mexican Americans (MA). Using a machine learning method with variants in GWAS catalog, we identified 229 to 246 pleiotropic variants for AI and 153 to 160 for MA for each SUD. Inflammation was the most enriched for MSUD and AUD in AIs. Neurological disorder was the most significantly enriched for CUD in both cohorts, and for AUD and stimulants in AIs. Of the select pleiotropic genes shared among substances-cohorts, multiple biological pathways implicated in SUD and other psychiatric disorders were enriched, including neurotrophic factors, immune responses, extracellular matrix, and circadian regulation. Shared pleiotropic genes were significantly up-regulated in brain regions playing important roles in SUD, down-regulated in esophagus mucosa, and differentially regulated in adrenal gland. This study fills a gap for pleiotropy detection in understudied admixed populations and identifies pleiotropic variants that may be potential targets of interest for SUD.

SMNN: batch effect correction for single-cell RNA-seq data via supervised mutual nearest neighbor detection.

Batch effect correction has been recognized to be indispensable when integrating single-cell RNA sequencing (scRNA-seq) data from multiple batches. State-of-the-art methods ignore single-cell cluster label information, but such information can improve the effectiveness of batch effect correction, particularly under realistic scenarios where biological differences are not orthogonal to batch effects. To address this issue, we propose SMNN for batch effect correction of scRNA-seq data via supervised mutual nearest neighbor detection. Our extensive evaluations in simulated and real datasets show that SMNN provides improved merging within the corresponding cell types across batches, leading to reduced differentiation across batches over MNN, Seurat v3 and LIGER. Furthermore, SMNN retains more cell-type-specific features, partially manifested by differentially expressed genes identified between cell types after SMNN correction being biologically more relevant, with precision improving by up to 841.0%.

Correction: An approach to integrating exome sequencing for fetal structural anomalies into clinical practice.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Projected t-SNE for batch correction.

MOTIVATION: Low-dimensional representations of high-dimensional data are routinely employed in biomedical research to visualize, interpret and communicate results from different pipelines. In this article, we propose a novel procedure to directly estimate t-SNE embeddings that are not driven by batch effects. Without correction, interesting structure in the data can be obscured by batch effects. The proposed algorithm can therefore significantly aid visualization of high-dimensional data. RESULTS: The proposed methods are based on linear algebra and constrained optimization, leading to efficient algorithms and fast computation in many high-dimensional settings. Results on artificial single-cell transcription profiling data show that the proposed procedure successfully removes multiple batch effects from t-SNE embeddings, while retaining fundamental information on cell types. When applied to single-cell gene expression data to investigate mouse medulloblastoma, the proposed method successfully removes batches related with mice identifiers and the date of the experiment, while preserving clusters of oligodendrocytes, astrocytes, and endothelial cells and microglia, which are expected to lie in the stroma within or adjacent to the tumours. AVAILABILITY AND IMPLEMENTATION: Source code implementing the proposed approach is available as an R package at https://github.com/emanuelealiverti/BC_tSNE, including a tutorial to reproduce the simulation studies. CONTACT: aliverti@stat.unipd.it.