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SpNeigh reveals intermediate cell populations near boundaries in <t>mouse</t> <t>brain</t> <t>Xenium</t> data. ( a ) Spatial plots showing different annotation types. Left: Cells colored by clusters with overlaid boundaries of cluster 2. Middle: Manual cluster-level annotations based on brain anatomy. Right: Reference-based single-cell annotations, with selected subtypes merged. CGE: caudal ganglionic eminence; MGE: medial ganglionic eminence. ( b ) Neighborhood analysis of cluster 2. Top: Boundary and ring regions. Bottom: Cells within boundary and ring regions for region 1, with donut plots showing cluster proportions (labels shown for proportions \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\ge$\end{document} 5%). ( c ) Expression of Slc17a7 and Sox10 in cluster 2 cells inside boundaries and surrounding rings. Slc17a7, a marker of cortical excitatory neurons, shows elevated expression in outer cells near the boundary. Sox10 is broadly expressed in oligodendrocytes and remains consistent across both inner and outer cells in cluster 2. ( d ) Boundary 1 of cluster 2 split into discrete edges. ( e ) Spatial weights relative to edge 2 for cortical cells. Black line indicates edge 2. ( f ) Top spatially varying genes identified by RunSpatialDE using weights from edge 2. ( g ) Expression of Ccn2 and Cplx3 near edge 2. Cells include cortical layer 4/5/6 neurons, L6b neurons, astrocytes, and oligodendrocytes. L6b cells are localized along edge 2.
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Overview of the SpNeigh workflow. ( a ) Input includes a spatial coordinate data frame ( x, y , cell, cluster) and a normalized expression matrix. Data can originate from platforms such as Xenium, <t>Visium</t> <t>HD,</t> MERFISH, or others. ( b ) Spatial boundary detection and neighborhood extraction. Left: Cluster boundaries are identified after removing spatial outliers based on local k-nearest neighbor density. Right: Ring regions are constructed by buffering outward from the cluster boundaries. Black lines denote cluster boundaries; blue lines indicate outer ring boundaries. ( c ) Spatial weight computation. Cells are assigned weights based on their distance to either the boundary (left) or the centroid (right) of the cluster using inverse distance decay. Weights range from 0 (far) to 1 (close), reflecting proximity. ( d ) Neighborhood composition and interaction analysis. Top: Pie chart showing the proportion of neighboring cell types within the rings. Bottom: Heatmap of spatial interaction scores between focal and neighboring clusters. ( e ) Downstream analyses enabled by SpNeigh. Left: Differential expression analysis between cells of the same cluster in the inner region versus the ring. Middle: Spatial differential expression analysis using smooth functions of distance-based weights. Right: Spatial enrichment analysis quantifying expression bias relative to spatial proximity.
Mouse Brain Visium Hd Dataset, supplied by 10X Genomics, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Overview of the SpNeigh workflow. ( a ) Input includes a spatial coordinate data frame ( x, y , cell, cluster) and a normalized expression matrix. Data can originate from platforms such as Xenium, <t>Visium</t> <t>HD,</t> MERFISH, or others. ( b ) Spatial boundary detection and neighborhood extraction. Left: Cluster boundaries are identified after removing spatial outliers based on local k-nearest neighbor density. Right: Ring regions are constructed by buffering outward from the cluster boundaries. Black lines denote cluster boundaries; blue lines indicate outer ring boundaries. ( c ) Spatial weight computation. Cells are assigned weights based on their distance to either the boundary (left) or the centroid (right) of the cluster using inverse distance decay. Weights range from 0 (far) to 1 (close), reflecting proximity. ( d ) Neighborhood composition and interaction analysis. Top: Pie chart showing the proportion of neighboring cell types within the rings. Bottom: Heatmap of spatial interaction scores between focal and neighboring clusters. ( e ) Downstream analyses enabled by SpNeigh. Left: Differential expression analysis between cells of the same cluster in the inner region versus the ring. Middle: Spatial differential expression analysis using smooth functions of distance-based weights. Right: Spatial enrichment analysis quantifying expression bias relative to spatial proximity.
Mouse Brain Endothelial Cells Bend3, supplied by ATCC, used in various techniques. Bioz Stars score: 99/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Overview of the SpNeigh workflow. ( a ) Input includes a spatial coordinate data frame ( x, y , cell, cluster) and a normalized expression matrix. Data can originate from platforms such as Xenium, <t>Visium</t> <t>HD,</t> MERFISH, or others. ( b ) Spatial boundary detection and neighborhood extraction. Left: Cluster boundaries are identified after removing spatial outliers based on local k-nearest neighbor density. Right: Ring regions are constructed by buffering outward from the cluster boundaries. Black lines denote cluster boundaries; blue lines indicate outer ring boundaries. ( c ) Spatial weight computation. Cells are assigned weights based on their distance to either the boundary (left) or the centroid (right) of the cluster using inverse distance decay. Weights range from 0 (far) to 1 (close), reflecting proximity. ( d ) Neighborhood composition and interaction analysis. Top: Pie chart showing the proportion of neighboring cell types within the rings. Bottom: Heatmap of spatial interaction scores between focal and neighboring clusters. ( e ) Downstream analyses enabled by SpNeigh. Left: Differential expression analysis between cells of the same cluster in the inner region versus the ring. Middle: Spatial differential expression analysis using smooth functions of distance-based weights. Right: Spatial enrichment analysis quantifying expression bias relative to spatial proximity.
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Overview of the SpNeigh workflow. ( a ) Input includes a spatial coordinate data frame ( x, y , cell, cluster) and a normalized expression matrix. Data can originate from platforms such as Xenium, <t>Visium</t> <t>HD,</t> MERFISH, or others. ( b ) Spatial boundary detection and neighborhood extraction. Left: Cluster boundaries are identified after removing spatial outliers based on local k-nearest neighbor density. Right: Ring regions are constructed by buffering outward from the cluster boundaries. Black lines denote cluster boundaries; blue lines indicate outer ring boundaries. ( c ) Spatial weight computation. Cells are assigned weights based on their distance to either the boundary (left) or the centroid (right) of the cluster using inverse distance decay. Weights range from 0 (far) to 1 (close), reflecting proximity. ( d ) Neighborhood composition and interaction analysis. Top: Pie chart showing the proportion of neighboring cell types within the rings. Bottom: Heatmap of spatial interaction scores between focal and neighboring clusters. ( e ) Downstream analyses enabled by SpNeigh. Left: Differential expression analysis between cells of the same cluster in the inner region versus the ring. Middle: Spatial differential expression analysis using smooth functions of distance-based weights. Right: Spatial enrichment analysis quantifying expression bias relative to spatial proximity.
Mouse Brain Coronal Frozen Sections, supplied by AMS Biotechnology, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Schematic workflow for <t>adult</t> <t>brain</t> <t>dissociation</t> and sequential isolation of cell types (microglia, astrocytes, and neurons). Tissue is harvested rapidly, placed in ice-cold DPBS with Ca 2+ and Mg 2+ , and cut into small pieces, then transferred to C-tubes and dissociated by a combination of enzymatic buffer solution containing papain with gentle mechanical dissociation, using a gentleMACS Octo Dissociator with heaters. Gradient centrifugation forms a compact ring containing debris (dead cells and myelin). After debris removal, red blood cells are lysed, and the dissociated cells are magnetically labeled with anti-CD11b MicroBeads against microglia. The labeled cells are passed through LS columns twice and placed on a magnetic stand. CD11b-positively selected cells remain in the column and are eluted with a plunger into a fresh tube. The negative flow-through is processed to isolate astrocytes labeled with anti-ACSA2 MicroBeads, and the process is repeated. For neuron isolation, the negative flow-through from astrocyte extraction is labeled with a non-neuronal biotin cocktail and anti-biotin MicroBeads, and the subsequent negative flow-through contains the neuron population. The arrows show the workflow handling of the cell suspensions. Image created in BioRender, Reddy, A. (2026) https://BioRender.com/mj9rqid , last accessed on 2 March 2026 and adapted from .
Miltenyi Adult Brain Dissociation, supplied by Miltenyi Biotec, used in various techniques. Bioz Stars score: 97/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Schematic workflow for <t>adult</t> <t>brain</t> <t>dissociation</t> and sequential isolation of cell types (microglia, astrocytes, and neurons). Tissue is harvested rapidly, placed in ice-cold DPBS with Ca 2+ and Mg 2+ , and cut into small pieces, then transferred to C-tubes and dissociated by a combination of enzymatic buffer solution containing papain with gentle mechanical dissociation, using a gentleMACS Octo Dissociator with heaters. Gradient centrifugation forms a compact ring containing debris (dead cells and myelin). After debris removal, red blood cells are lysed, and the dissociated cells are magnetically labeled with anti-CD11b MicroBeads against microglia. The labeled cells are passed through LS columns twice and placed on a magnetic stand. CD11b-positively selected cells remain in the column and are eluted with a plunger into a fresh tube. The negative flow-through is processed to isolate astrocytes labeled with anti-ACSA2 MicroBeads, and the process is repeated. For neuron isolation, the negative flow-through from astrocyte extraction is labeled with a non-neuronal biotin cocktail and anti-biotin MicroBeads, and the subsequent negative flow-through contains the neuron population. The arrows show the workflow handling of the cell suspensions. Image created in BioRender, Reddy, A. (2026) https://BioRender.com/mj9rqid , last accessed on 2 March 2026 and adapted from .
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SpNeigh reveals intermediate cell populations near boundaries in mouse brain Xenium data. ( a ) Spatial plots showing different annotation types. Left: Cells colored by clusters with overlaid boundaries of cluster 2. Middle: Manual cluster-level annotations based on brain anatomy. Right: Reference-based single-cell annotations, with selected subtypes merged. CGE: caudal ganglionic eminence; MGE: medial ganglionic eminence. ( b ) Neighborhood analysis of cluster 2. Top: Boundary and ring regions. Bottom: Cells within boundary and ring regions for region 1, with donut plots showing cluster proportions (labels shown for proportions \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\ge$\end{document} 5%). ( c ) Expression of Slc17a7 and Sox10 in cluster 2 cells inside boundaries and surrounding rings. Slc17a7, a marker of cortical excitatory neurons, shows elevated expression in outer cells near the boundary. Sox10 is broadly expressed in oligodendrocytes and remains consistent across both inner and outer cells in cluster 2. ( d ) Boundary 1 of cluster 2 split into discrete edges. ( e ) Spatial weights relative to edge 2 for cortical cells. Black line indicates edge 2. ( f ) Top spatially varying genes identified by RunSpatialDE using weights from edge 2. ( g ) Expression of Ccn2 and Cplx3 near edge 2. Cells include cortical layer 4/5/6 neurons, L6b neurons, astrocytes, and oligodendrocytes. L6b cells are localized along edge 2.

Journal: NAR Genomics and Bioinformatics

Article Title: SpNeigh: spatial neighborhood and differential expression analysis for high-resolution spatial transcriptomics

doi: 10.1093/nargab/lqag039

Figure Lengend Snippet: SpNeigh reveals intermediate cell populations near boundaries in mouse brain Xenium data. ( a ) Spatial plots showing different annotation types. Left: Cells colored by clusters with overlaid boundaries of cluster 2. Middle: Manual cluster-level annotations based on brain anatomy. Right: Reference-based single-cell annotations, with selected subtypes merged. CGE: caudal ganglionic eminence; MGE: medial ganglionic eminence. ( b ) Neighborhood analysis of cluster 2. Top: Boundary and ring regions. Bottom: Cells within boundary and ring regions for region 1, with donut plots showing cluster proportions (labels shown for proportions \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\ge$\end{document} 5%). ( c ) Expression of Slc17a7 and Sox10 in cluster 2 cells inside boundaries and surrounding rings. Slc17a7, a marker of cortical excitatory neurons, shows elevated expression in outer cells near the boundary. Sox10 is broadly expressed in oligodendrocytes and remains consistent across both inner and outer cells in cluster 2. ( d ) Boundary 1 of cluster 2 split into discrete edges. ( e ) Spatial weights relative to edge 2 for cortical cells. Black line indicates edge 2. ( f ) Top spatially varying genes identified by RunSpatialDE using weights from edge 2. ( g ) Expression of Ccn2 and Cplx3 near edge 2. Cells include cortical layer 4/5/6 neurons, L6b neurons, astrocytes, and oligodendrocytes. L6b cells are localized along edge 2.

Article Snippet: Mouse brain tiny Xenium dataset: https://www.10xgenomics.com/datasets/fresh-frozen-mouse-brain-for-xenium-explorer-demo-1-standard .

Techniques: Single Cell, Expressing, Marker

Overview of the SpNeigh workflow. ( a ) Input includes a spatial coordinate data frame ( x, y , cell, cluster) and a normalized expression matrix. Data can originate from platforms such as Xenium, Visium HD, MERFISH, or others. ( b ) Spatial boundary detection and neighborhood extraction. Left: Cluster boundaries are identified after removing spatial outliers based on local k-nearest neighbor density. Right: Ring regions are constructed by buffering outward from the cluster boundaries. Black lines denote cluster boundaries; blue lines indicate outer ring boundaries. ( c ) Spatial weight computation. Cells are assigned weights based on their distance to either the boundary (left) or the centroid (right) of the cluster using inverse distance decay. Weights range from 0 (far) to 1 (close), reflecting proximity. ( d ) Neighborhood composition and interaction analysis. Top: Pie chart showing the proportion of neighboring cell types within the rings. Bottom: Heatmap of spatial interaction scores between focal and neighboring clusters. ( e ) Downstream analyses enabled by SpNeigh. Left: Differential expression analysis between cells of the same cluster in the inner region versus the ring. Middle: Spatial differential expression analysis using smooth functions of distance-based weights. Right: Spatial enrichment analysis quantifying expression bias relative to spatial proximity.

Journal: NAR Genomics and Bioinformatics

Article Title: SpNeigh: spatial neighborhood and differential expression analysis for high-resolution spatial transcriptomics

doi: 10.1093/nargab/lqag039

Figure Lengend Snippet: Overview of the SpNeigh workflow. ( a ) Input includes a spatial coordinate data frame ( x, y , cell, cluster) and a normalized expression matrix. Data can originate from platforms such as Xenium, Visium HD, MERFISH, or others. ( b ) Spatial boundary detection and neighborhood extraction. Left: Cluster boundaries are identified after removing spatial outliers based on local k-nearest neighbor density. Right: Ring regions are constructed by buffering outward from the cluster boundaries. Black lines denote cluster boundaries; blue lines indicate outer ring boundaries. ( c ) Spatial weight computation. Cells are assigned weights based on their distance to either the boundary (left) or the centroid (right) of the cluster using inverse distance decay. Weights range from 0 (far) to 1 (close), reflecting proximity. ( d ) Neighborhood composition and interaction analysis. Top: Pie chart showing the proportion of neighboring cell types within the rings. Bottom: Heatmap of spatial interaction scores between focal and neighboring clusters. ( e ) Downstream analyses enabled by SpNeigh. Left: Differential expression analysis between cells of the same cluster in the inner region versus the ring. Middle: Spatial differential expression analysis using smooth functions of distance-based weights. Right: Spatial enrichment analysis quantifying expression bias relative to spatial proximity.

Article Snippet: Mouse brain Visium HD dataset: https://www.10xgenomics.com/datasets/visium-hd-cytassist-gene-expression-mouse-brain-fresh-frozen .

Techniques: Expressing, Extraction, Construct, Quantitative Proteomics

Schematic workflow for adult brain dissociation and sequential isolation of cell types (microglia, astrocytes, and neurons). Tissue is harvested rapidly, placed in ice-cold DPBS with Ca 2+ and Mg 2+ , and cut into small pieces, then transferred to C-tubes and dissociated by a combination of enzymatic buffer solution containing papain with gentle mechanical dissociation, using a gentleMACS Octo Dissociator with heaters. Gradient centrifugation forms a compact ring containing debris (dead cells and myelin). After debris removal, red blood cells are lysed, and the dissociated cells are magnetically labeled with anti-CD11b MicroBeads against microglia. The labeled cells are passed through LS columns twice and placed on a magnetic stand. CD11b-positively selected cells remain in the column and are eluted with a plunger into a fresh tube. The negative flow-through is processed to isolate astrocytes labeled with anti-ACSA2 MicroBeads, and the process is repeated. For neuron isolation, the negative flow-through from astrocyte extraction is labeled with a non-neuronal biotin cocktail and anti-biotin MicroBeads, and the subsequent negative flow-through contains the neuron population. The arrows show the workflow handling of the cell suspensions. Image created in BioRender, Reddy, A. (2026) https://BioRender.com/mj9rqid , last accessed on 2 March 2026 and adapted from .

Journal: Cells

Article Title: Astro-Versus Microglia-Enriched Transcriptomes from Aged Atxn2 -CAG100-Knockin Mice Suggest Underlying Pathology of RNA Processing at Ribosomes, and Possibly at U-Bodies

doi: 10.3390/cells15080699

Figure Lengend Snippet: Schematic workflow for adult brain dissociation and sequential isolation of cell types (microglia, astrocytes, and neurons). Tissue is harvested rapidly, placed in ice-cold DPBS with Ca 2+ and Mg 2+ , and cut into small pieces, then transferred to C-tubes and dissociated by a combination of enzymatic buffer solution containing papain with gentle mechanical dissociation, using a gentleMACS Octo Dissociator with heaters. Gradient centrifugation forms a compact ring containing debris (dead cells and myelin). After debris removal, red blood cells are lysed, and the dissociated cells are magnetically labeled with anti-CD11b MicroBeads against microglia. The labeled cells are passed through LS columns twice and placed on a magnetic stand. CD11b-positively selected cells remain in the column and are eluted with a plunger into a fresh tube. The negative flow-through is processed to isolate astrocytes labeled with anti-ACSA2 MicroBeads, and the process is repeated. For neuron isolation, the negative flow-through from astrocyte extraction is labeled with a non-neuronal biotin cocktail and anti-biotin MicroBeads, and the subsequent negative flow-through contains the neuron population. The arrows show the workflow handling of the cell suspensions. Image created in BioRender, Reddy, A. (2026) https://BioRender.com/mj9rqid , last accessed on 2 March 2026 and adapted from .

Article Snippet: The subsequent protocol is adapted from Miltenyi adult brain dissociation (Miltenyi, #130-107-677, Miltenyi Biotech, Bergisch Gladbach, Germany): adult neuron isolation, mouse (Miltenyi, #130-126-603); Anti-ACSA2 MicroBead Kit, mouse (Miltenyi, #130-097-679); and CD11b (Microglia) MicroBeads, human and mouse (Miltenyi, #130-093-636)—see [ , ].

Techniques: Isolation, Gentle, Gradient Centrifugation, Labeling, Extraction

Schematic workflow for adult brain dissociation and sequential isolation of cell types (microglia, astrocytes, and neurons). Tissue is harvested rapidly, placed in ice-cold DPBS with Ca 2+ and Mg 2+ , and cut into small pieces, then transferred to C-tubes and dissociated by a combination of enzymatic buffer solution containing papain with gentle mechanical dissociation, using a gentleMACS Octo Dissociator with heaters. Gradient centrifugation forms a compact ring containing debris (dead cells and myelin). After debris removal, red blood cells are lysed, and the dissociated cells are magnetically labeled with anti-CD11b MicroBeads against microglia. The labeled cells are passed through LS columns twice and placed on a magnetic stand. CD11b-positively selected cells remain in the column and are eluted with a plunger into a fresh tube. The negative flow-through is processed to isolate astrocytes labeled with anti-ACSA2 MicroBeads, and the process is repeated. For neuron isolation, the negative flow-through from astrocyte extraction is labeled with a non-neuronal biotin cocktail and anti-biotin MicroBeads, and the subsequent negative flow-through contains the neuron population. The arrows show the workflow handling of the cell suspensions. Image created in BioRender, Reddy, A. (2026) https://BioRender.com/mj9rqid , last accessed on 2 March 2026 and adapted from .

Journal: Cells

Article Title: Astro-Versus Microglia-Enriched Transcriptomes from Aged Atxn2 -CAG100-Knockin Mice Suggest Underlying Pathology of RNA Processing at Ribosomes, and Possibly at U-Bodies

doi: 10.3390/cells15080699

Figure Lengend Snippet: Schematic workflow for adult brain dissociation and sequential isolation of cell types (microglia, astrocytes, and neurons). Tissue is harvested rapidly, placed in ice-cold DPBS with Ca 2+ and Mg 2+ , and cut into small pieces, then transferred to C-tubes and dissociated by a combination of enzymatic buffer solution containing papain with gentle mechanical dissociation, using a gentleMACS Octo Dissociator with heaters. Gradient centrifugation forms a compact ring containing debris (dead cells and myelin). After debris removal, red blood cells are lysed, and the dissociated cells are magnetically labeled with anti-CD11b MicroBeads against microglia. The labeled cells are passed through LS columns twice and placed on a magnetic stand. CD11b-positively selected cells remain in the column and are eluted with a plunger into a fresh tube. The negative flow-through is processed to isolate astrocytes labeled with anti-ACSA2 MicroBeads, and the process is repeated. For neuron isolation, the negative flow-through from astrocyte extraction is labeled with a non-neuronal biotin cocktail and anti-biotin MicroBeads, and the subsequent negative flow-through contains the neuron population. The arrows show the workflow handling of the cell suspensions. Image created in BioRender, Reddy, A. (2026) https://BioRender.com/mj9rqid , last accessed on 2 March 2026 and adapted from .

Article Snippet: DPBS was replaced with 1950 μL of enzyme mix 1, as mentioned in the Miltenyi adult brain dissociation kit (Miltenyi #130-107-677).

Techniques: Isolation, Gentle, Gradient Centrifugation, Labeling, Extraction