The devCCF developmental mouse atlas includes embryonic and postnatal developmental brain structures parallel to the Allen Mouse Brain Atlas CCF. The epDev developmental mouse atlas includes postnatal 3D mappings of GABAergic, microglial, and cortical cell types.  

NeuroInfo has an open, documented atlas architecture that allows researchers to utilize other digital atlases. If you are interested in other support for other atlases, just let us know.

Please contact nate.oconnor@mbfbioscience.com for information on using these exciting developing brain resources for mapping structures and cell populations in NeuroInfo.

References

Developmental mouse brain common coordinate framework – PMC

epDevAtlas: Mapping GABAergic cells and microglia in postnatal mouse brains | bioRxiv

The Allen Mouse Brain Common Coordinate Framework: A 3D Reference Atlas: Cell

The post Developmental Mouse Brain Atlases are now available in NeuroInfo appeared first on MBF Bioscience.

Key Updates

• Offline Licensing Update
  To ensure uninterrupted use of Neurolucida® 360 on systems without internet access, reactivation is now required.

• Activation Process:
• • To obtain a new offline Activation Key, please fill out this webform.
  • Once submitted, detailed instructions for completing the offline authorization process will be provided.

• Support Documentation:
  Step-by-step instructions for offline activation are available in the Neurolucida 360 User Guide for your convenience.

New Features and Enhancements

• Enhanced Vessel Tracing: Improvements to the vessel tracing options in the 2D window for better precision.
• Streamlined Continuous Tracing: Automove now repositions focus seamlessly when using Continuous tracing.
• Puncta Setup Updates: Color channel selection now begins at number 1, simplifying the setup process.
• 2D and 3D Synchronization: Improved synchronization for contours delineated across both dimensions.
• Image Organization: Image file paths are now displayed in the Image Organizer for easier management.
• Dynamic Movie Adjustments: You can now change image-display settings for image slices and partial projections during a movie.
• 3D Scale Bar Display: Movies can now feature a 3D scale bar for added clarity.
• Movie Mode Interface Enhancements: Other improvements have been implemented to make recording and playback more intuitive.

For additional support or questions, please contact our customer support team at support@mbfbioscience.com.

The post Neurolucida 360 Software Update: Version 2024.2.2 appeared first on MBF Bioscience.

The authors obtained post-mortem human brain samples from the Brodmann area 28 (BA28) entorhinal cortex (EC) of subjects exhibiting a range of Alzheimer’s disease (AD) pathology and categorized into 3 groups based on cognition and AD pathology: normal cognition, noAD pathology; normal cognition with moderate to severe AD pathology, and definite AD cases. Synaptosome fractions were characterized biochemically, and proteomic profiles were determined using liquid chromatography coupled to mass spectrometry. Weighted gene co-expression network analysis was used to generate a protein co-expression network and identify protein modules (co-expressed proteins) that were present in the different cognition/AD pathology categories.

In parallel, tissue samples from the same brain area were fixed and processed for dendrite imaging using Golgi-Cox staining. Dendritic segments of pyramidal neurons from layers 2 and 3 of BA28 from each category of cognition/AD pathology were imaged with a 60X/1.40 NA oil-immersion objective using a brightfield microscope. The resulting 3D image stacks were opened in Neurolucida 360 and dendrite and dendritic spine morphologies were reconstructed using semi-automatic and automatic functions. Spines were automatically classified as stubby, mushroom, or filopodia. Volumetric measurements of the spine, as well as the density of each spine type per dendrite length were extracted with Neurolucida Explorer.

Figure: Overview of workflow. Synaptosomes were isolated from postmortem human BA28 entorhinal cortex (EC) and subjected to liquid chromatography tandem mass spectrometry-based proteomics. Weighted Gene Co-Expression Network Analysis (WGCNA) was used to generate a network of protein co-expression modules. BA28 EC samples were also Golgi stained and z-stacks of dendritic segments were imaged and digitally reconstructed to obtain measurements of dendritic spine density and morphology. Module eigenprotein values were correlated with dendritic spine metrics. The hub protein of a module significantly correlated with a dendritic spine metric would be selected for functional validation by CRISPR activation in rat primary hippocampal neurons.

The authors then correlated dendritic spine measurements with module eigenprotein expression from the proteomic analysis to integrate the two data categories. Among the results, one particular protein module stood out; it was consistently present in both AD and non-AD tissue, and was positively correlated with thin dendritic spine length, especially thin spines. Twinfilin2, the hub protein within this module, has a well-established role in modulating the cytoskeleton, specifically the protein actin. When the authors looked at neurons from rats grown in culture with different amounts of TWF2, they found those with more TWF2 grew longer thin-spines. This was the only type of spine affected by TWF2, demonstrating what the authors call a remarkable specificity regarding the ability of their cross-platform analysis to identify the functions of proteins.

Looking ahead, the researchers have identified many proteins organized in modules with hub-proteins, some that are expressed equally in AD and non-AD cases, and some that are not. They are in a good position to determine which of these hub proteins merit further study using functional analyses.

The comprehensive workflow employed by the researchers opens up new possibilities for unraveling the mysteries of neuronal function and holds immense potential for advancing our knowledge of diverse neurological conditions. As we delve deeper into the complex world of neuroscience, the connection between technology and scientific inquiry continues to illuminate the path towards groundbreaking discoveries.

Reference:

Walker, C. K., Greathouse, K. M., Tuscher, J. J., Dammer, E. B., Weber, A. J., Liu, E., Curtis, K. A., Boros, B. D., Freeman, C. D., Seo, J. V., Ramdas, R., Hurst, C., Duong, D. M., Gearing, M., Murchison, C. F., Day, J. J., Seyfried, N. T., & Herskowitz, J. H. (2023). Cross-platform synaptic network analysis of human entorhinal cortex identifies TWF2 as a modulator of dendritic spine length. The Journal of Neuroscience. https://doi.org/10.1523/jneurosci.2102-22.2023

The post From Proteins to Dendritic Spines: Neurolucida 360 Plays a Crucial Role in Advancing Neuroscience appeared first on MBF Bioscience.

At MBF Bioscience, democratizing neuroscience has been part of our DNA since our founding 35 years ago when we launched Neurolucida. Our goal was to provide affordable, cutting-edge technology to neuroscience laboratories in every institution worldwide so that neuroscientists could make discoveries without having huge research budgets or having computer programmers on staff.

When we started MBF, advances in Neuroscience research were driven by individual investigators.  More and more “big science” is having a greater impact on the field.  This transition began around the turn of the century, with the discovery that there are more than 20,000 unique genes in the brain. Advances in molecular biologic, neuroanatomical, neurophysiologic and computational techniques employed by teams of researchers at research institutes or by networked labs at multiple universities have analyzed the complexity of the brain’s neural circuits comprised of thousands of unique neuron subtypes.  Amongst “big science” projects are those that produced whole brain maps of the expression of 20,000 genes, the connectome of hundreds of neuron subtypes, the axonal projections of thousands of individual neurons and the physiologic characteristics of hundreds of genetically unique neuron subtypes.  Typically, publication of these projects includes upwards of 50 authors, indicative of the effort required.  The value of these large-scale databases and data sets is the ability to extract information about specific brain circuits to understand how their function generates behavior.   This is the work still primarily driven by individual investigators, who continue to make conceptual advances in the field now aided by the resources provided by “big science”. 

One of our long-time users, Dr. Charles Gerfen, has been involved in several “big science” projects, including the GENSAT project at NIMH/NINDS with Dr. Nat Heintz at Rockefeller University that generated 300 Cre-expressing transgenic mouse lines, the Allen Institute’s Mouse Connectome study that mapped the projections of neuron subtypes from 1000 brain areas, and the HHMI Janelia Mouse Light Project that traced the axonal projections of 900 individual cortical neurons.  With teams of researchers these projects each mapped the distribution and connections of diverse subtypes of neurons into a standard whole brain atlas to provide a searchable “google map-like” database of brain circuits. 

With advice from Dr. Gerfen, MBF developed a new software platform called NeuroInfo, that incorporates functions to allow individual researchers to map their neuroanatomical data into a standard atlas framework.  NeuroInfo is becoming a widely used platform for individual researchers to use a “big science” approach to study their specific biologic questions of interest to understand how neural circuits are related to neurologic and mental disorders. 

At MBF Bioscience, we’re committed to providing the best products and value to neuroscientists in labs of all sizes. We’re focused on making our products something that labs can rely on for years to come at a reasonable cost with great support. We’re proud of our own accomplishments and how we’ve adapted and evolved over the years to bring the most advanced technology at affordable prices to labs around the world.

The post Letter from the President: The Democratization of Neuroscience – Big Science in Individual Labs appeared first on MBF Bioscience.

In a study published in 2015, Bansel et al. found that long-lived C. elegans mutants exhibited a higher proportion of life in an unhealthy state compared to non-mutants. In their 2017 publication, Rollins et al. sought to better understand the relationship between lifespan and quality of life in C. elegans mutants. The authors used two models to assess health span in short-lived mutants: one focused on age-dependent factors such as locomotion, maximum bending amplitude, and thermo-tolerance; the other examined the effects of extrinsic forces over time, including accumulation of autofluorescence and pharyngeal pumping.

To track the worms and obtain data on size and behavior including speed of locomotion and bending angle, the researchers utilized WormLab® software. They found that short-lived mutants spent less time in a healthy state compared to non-mutants, when locomotion markers were used for the evaluation.   Unexpectedly, however, short-lived mutants exhibited thermo-tolerance for a longer percentage of life span than wild-type worms, suggesting that these mutants may have an advantage in this particular measure of health span.

The authors propose a new metric that combines survival rate and health performance to more accurately score health, taking into account both age-dependent and time-dependent factors. This approach could help to better understand the relationship between lifespan and quality of life in model organisms and could have implications for future research on aging and longevity.

In conclusion, the study of short-lived C. elegans mutants provides valuable insights into the relationship between life span and quality of life. The use of two models to assess health and the proposal of a new metric to score health highlight the complexity of this relationship and the need for further research to fully understand it. As we continue to strive for longer, healthier lives, the use of model organisms like C. elegans will undoubtedly remain essential to this research as we aim to promote healthy aging and unlock the secrets of aging.

Learn more about the WormLab software

Reference:

Rollins, J. A., Howard, A. C., Dobbins, S. K., Washburn, E. H., & Rogers, A. N. (2017). Assessing health span in Caenorhabditis elegans: Lessons from short-lived mutants. The Journals of Gerontology: Series A, 72(4), 473–480. https://doi.org/10.1093/gerona/glw248

The post Exploring the Relationship between Lifespan and Quality of Life in C. Elegans Mutants appeared first on MBF Bioscience.

At Dr. Patrick R. Hof’s lab at the Icahn School of Medicine at Mount Sinai, researchers imaged the cerebral cortex using light-sheet fluorescence microscopy and quantified the number of neurons, including those that express proteins involved in Alzheimer’s disease and schizophrenia, using the Image Volume Fractionator¹. This work marks the beginning of an important and ambitious project to build an atlas of cortical cells, using a multi-resolution imaging pipeline. At the pipeline’s highest level of resolution, both the Image Volume Fractionator, for use with thick sections of cleared tissue, and the Optical Fractionator, for much thinner sections, are being used to estimate cell number. The researchers will also use automatic cell detection and plan to compare results obtained using the three methods.

In the paper A Multimodal Imaging and Analysis Pipeline for Creating a Cellular Census of the Human Cerebral Cortex¹, the authors describe the beginning of the effort to build a census of the human cerebral cortex, a laminar structure, that contains layers comprised of different cell types visible using high resolution microscopy. There are a number of different neuronal cell types in each layer, including projection neurons and interneurons, as well as excitatory and inhibitory neurons. Layers can be identified based on different proteins contained in certain cells using fluorescence immunohistochemistry. Calretinin, a calcium-binding protein, is found in a subpopulation of the inhibitory interneurons that contain GABA. Neurofilament protein, which can be found in the cytoskeleton, makes up 30 percent of cortex cells. Parvalbumin, another calcium-binding protein, is also found in a subset of cortical cells.

The cells in the cortex have a purpose that is supported by their neurochemical and anatomical characteristics. Here are two examples involving Alzheimer’s disease and schizophrenia. Calretinin-positive cells in the cortex are spared in Alzheimer’s disease², but neurofilament protein-positive cells degenerate, and that degeneration may predict cognitive decline³. The parvalbumin-containing basket cell is an inhibitory GABAergic interneuron in the cortex that inhibits the main output cell—the pyramidal neuron. Problems with this cell type may affect gamma oscillations, leading to the deficits in cognitive control that accompany schizophrenia⁴.

Wouldn’t it be valuable to have an atlas or census of the cortex that is “zoomable” like a GPS map, and shows the cell types and their connections? Hof. et.al., demonstrate that this is possible using three imaging techniques at increasing resolutions (Fig. 1).

Fig. 1 The three imaging modalities used in this study. Magnetic Resonance Imaging (MRI) is the lowest resolution. Optical Coherence Tomography (OCT) is the mid-resolution. Light Sheet Fluorescence Microscopy (LSFM) is the highest resolution. The Image Volume Fractionator probe is carried out using LSFM. Those images are registered to eliminate distortion to help match them to OCT and the MRI images.

At the most highly resolved level, LSFM, two unbiased stereology techniques are used to build a census inside the atlas: the Optical Fractionator and the new, Image Volume Fractionator. The latter is made possible by tissue clearing methods, which in turn allows for the use of tissue sections that are, in this case, 10 times thicker than for the Optical Fractionator (Fig. 2).

Fig. 2 The Image Volume Fractionator (IVF) was designed to be used on thick sections or large intact specimens. It is much more efficient than working on traditional histological sections that were not cleared and therefore need to be, in this case, 10 times thinner. N is the estimate of number of cells. Systematic random sampling and disector rules are followed while counting.

Since the LSFM images are ten times thicker than the thinner sections needed in the absence of tissue clearing, counting with the Image Volume Fractionator probe can be done more quickly. It is much more efficient to count cells in one large image than in ten separate thinner tissue sections. There is also less sectioning artifact, which helps with registering the higher resolution LSFM images back to the larger volume MRI images.

We are excited to see this new use of the Image Volume Fractionator, which increases efficiency and reduces imaging distortions from physical sectioning. The potential that cleared tissue offers for increasing efficiency is great, but is still largely untapped. As this method is used more frequently, we look forward to hearing feedback from the research community to further improve the capabilities and usability of the Image Volume Fractionator in Stereo Investigator – Cleared Tissue Edition.

References:

1) A Multimodal Imaging and Analysis Pipeline for Creating a Cellular Census of the Human Cerebral Cortex 2021, Constantini, et al., https://www.biorxiv.org/content/10.1101/2021.10.20.464979v1

2) Hof, P. R., Nimchinsky, E. A., Celio, M. R., Bouras, C. & Morrison, J. H. Calretinin, Immunoreactive neocortical interneurons are unaffected in Alzheimer’s disease. 861 Neurosci Lett 152, 145-148 (1993).

3) Bussiere, T. et al. Progressive degeneration of nonphosphorylated neurofilament protei enriched pyramidal neurons predicts cognitive impairment in Alzheimer’s disease: Stereologic analysis of prefrontal cortex area 9. Journal of Comparative Neurology (2003).

4) Glausier, J. R., Fish, K. N. & Lewis, D. A. Altered parvalbumin basket cell inputs in the dorsolateral prefrontal cortex of schizophrenia subjects. Mol Psychiatry 19, 30-36 (2014). Lewis, D. A., Curley, A. A., Glausier, J. R. & Volk, D. W. Cortical parvalbumin interneurons and cognitive dysfunction in schizophrenia. Trends Neurosci 35, 57-67 (2012).

The post Researchers quantify cortical cell numbers in cleared tissue with new unbiased stereology technique appeared first on MBF Bioscience.

The field of applied mathematics known as “topological data analysis” uses topological methods to uncover patterns in large, high-dimensional datasets that might otherwise go unnoticed. In a collaborative effort led by the Blue Brain Project, directed by Dr. Henry Markram, researchers used topological data analysis to develop an objective method of classifying pyramidal cells based on their morphology.

The morphology and behavior of neurons can vary widely, and developing methods for classifying and differentiating between different types of cells has proven advantageous for scientific research. However, investigators tend to devise their own methods, which often results in contradictory classifications of the same tissue between researchers, and even repeat trials of the same tissue with the same investigator can produce contradictory results (DeFelipe et al, 2013). To avoid investigator biases, the Blue Brain scientists used the Topological Morphology Descriptor (TMD) algorithm to analyze pyramidal cells reconstructed with Neurolucida 360. The aim of the TMD algorithm is to produce a simplified representation of the cell morphology based on the topological descriptors of the cell’s shape rather than visual inspection and feature selection (Kanari et al, 2018). Using the algorithm, they made the same differentiations between cell types that experts had made previously, but they also observed two types of pyramidal cells typically identified by experts, which were not able to be differentiated by the topological data available, suggesting that these cell types either represent opposite ends of a continuum of expression, or that experts are noticing topological features that were not captured by this version of the algorithm.

Three PC types/subtypes in Layer 2. (A) Exemplar reconstructed morphologies of PC dendrites: the apical dendrite is presented in purple and the basal dendrites in red. (B) Polar plot analysis of dendritic branches (apical in purple, basal in red). Tufted PCs are oriented towards the pia and the inverted PCs in the opposite direction as they project towards the white matter. (C) The Topological Morphology Descriptor (TMD) of apical dendrites characterizes the spatial distribution of branches with respect to the radial distance from the neuronal soma. The average persistence images (per type of PC) illustrate the average dendritic arbor density around the soma.

This study represents an important step towards objective classification of cells, and there is broad potential for expanding on this work. One challenge the authors discuss is the volume of data available for developing and validating a TMD algorithm—some cell types simply haven’t been observed and digitally reconstructed as often as the pyramidal cells studied here. However, the analytical potential of the TMD lies in its broad applicability. Since it operates on 3D tree-like structures, any neuron digitally reconstructed in 3D can in theory be analyzed by the TMD algorithm. The authors leave us with an open invitation to work with and expand the TMD algorithm.

In a very recent publication (Gillespie et al, 2022) researchers have noted that the US Brain Initiative Cell Census Network, Human Cell Atlas, Blue Brain Project, and others are generating vast amounts of data and characterizing large numbers of neurons throughout the nervous system. They have proposed Neuron Phenotype Ontology: A FAIR Approach to Proposing and Classifying Neuronal Types.

At MBF Bioscience we’re also working to establish consensus within the neuroscience community, and have published a full specification of our neuromorphological file format used to store these 3D digital reconstructions. We invite the global community to make use of this format for storing their own digital reconstructions.

And if you’re interested in learning more about obtaining detailed reconstructions of cells in your samples with Neurolucida 360, request a free demonstration of Neurolucida 360 from our team of experts.

References:

DeFelipe, J., López-Cruz, P., Benavides-Piccione, R. et al. New insights into the classification and nomenclature of cortical GABAergic interneurons. Nat Rev Neurosci 14, 202–216 (2013). https://doi.org/10.1038/nrn3444

Gillespie, T.H., Tripathy, S.J., Sy, M.F. et al. The Neuron Phenotype Ontology: A FAIR Approach to Proposing and Classifying Neuronal Types. Neuroinform (2022). https://doi.org/10.1007/s12021-022-09566-7

Kanari, L., Dłotko, P., Scolamiero, M. et al. A Topological Representation of Branching Neuronal Morphologies. Neuroinform 16, 3–13 (2018). https://doi.org/10.1007/s12021-017-9341-1

Lida Kanari, Srikanth Ramaswamy, Ying Shi, Sebastien Morand, Julie Meystre, Rodrigo Perin, Marwan Abdellah, Yun Wang, Kathryn Hess, Henry Markram, Objective Morphological Classification of Neocortical Pyramidal Cells, Cerebral Cortex, Volume 29, Issue 4, April 2019, Pages 1719–1735, https://doi.org/10.1093/cercor/bhy339

The post Blue Brain scientists develop pyramidal cell classification system using neurons reconstructed with Neurolucida 360 appeared first on MBF Bioscience.

The file format is used in our products for neuroscience research for important applications such as digital neuron tracing, brain mapping and stereological analyses. MBF Bioscience products, including Neurolucida, Neurolucida 360, Stereo Investigator, Vesselucida 360, and NeuroInfo use this neuromorphological file format.

This file format has evolved over several decades through input and requests from many scientists who’ve been using our products. The current format is truly a collaborative effort between MBF and our users.

“We are very pleased to have received this endorsement from the INCF. It recognizes our ongoing efforts in supporting open and FAIR neuroscience, and our commitment to supporting neuroscience researchers. We’ve established rigorous standards and processes for the file format so that it can be confidently used by the entire research community”, said Jack Glaser, President of MBF Bioscience.

What does this mean for researchers who use MBF products? It expands opportunities for data sharing between individual researchers, laboratories, and within larger collaborative research initiatives. Also, it will be easier for third-party software tools to be developed and maintained that extend the usefulness of the data generated by MBF products.

The official INCF announcement stated, “The committee is pleased to see an open format from a commercial entity go through the endorsement process, and applaud MBF Bioscience for taking this very important step in support of open and FAIR neuroscience. The committee considers the governance process for MBF Bioscience’s neuromorphological file format to be well elaborated, with a sufficient mechanism for the user community to request format updates.”

MBF Bioscience and the INCF will work together to further improve the FAIRness of the standard, including implementation of the governance policy and modification of the standard’s license from the CC-BY-ND-NC to a CC-BY-ND.

The standard number is INCFSN-22-01.

Read the review report, with community feedback in comments: https://f1000research.com/documents/10-712

Read the full INCF endorsement here: https://www.incf.org/blog/incf-endorses-mbf-neuromorphological-file-format

Read a recent publication on the format:

A.E. Sullivan, S. J. Tappan, P. J. Angstman, A. Rodriguez, G. C. Thomas, D. M. Hoppes, M. A. Abdul-Karim, M. L. Heal & Jack R. Glaser. A Comprehensive, FAIR File Format for Neuroanatomical Structure Modeling. Neuroinform (2021). https://doi.org/10.1007/s12021-021-09530-x

The post INCF endorses the MBF Bioscience neuromorphological file format appeared first on MBF Bioscience.

“NeuroInfo already includes extensive analysis capabilities for mouse brain research by standardizing measurements on brain volumes to the Allen Mouse Brain Atlas. The inclusion of a rat atlas in NeuroInfo expands brain research to more complicated behavioral and disease models,” says MBF Bioscience Senior Product Manager Dr. Nathan J. O’Connor.

Image Credit: Harvey Karten, PhD

Successful neuroscience research projects in big data areas such as transcriptomics, proteomics, and connectomics rely on efficient methodologies and data reporting. NeuroInfo uses deep learning and automated image processing workflows guided by widely used standardized atlases to repeatably produce and report outcomes that can be combined and compared across animals, cohorts, and laboratories.

Learn more about NeuroInfo.

The post NeuroInfo Now Analyzes Rat Brains appeared first on MBF Bioscience.

Representative maps of resistance networks from feed artery to terminal

In a study published in the Journal of Physiology, researchers at the University of Missouri, Columbia, describe short- and long-term capillary damage and recovery after acute skeletal muscle injury. The researchers observed that two to three days after injury, surviving microvessel fragments began to sprout new capillaries, and that five days post-injury new functional capillary networks formed.

In this study, researchers used Vesselucida from MBF Bioscience to characterize changes in skeletal microvasculature throughout a mouse model of muscle injury. 3D reconstructions of injured and recovering vessels in whole-mount preparations of the gluteus maximus muscle at various time points reveal the chronological degeneration and remodeling of the vascular network before and after injury.

“Using Vesselucida, we were able to assess changes in resistance network architecture during muscle regeneration for the first time,” said Dr. Nicole Jacobsen. “We were limited by other imaging methods due to the network size and location of arteriolar networks within skeletal muscle. Vesselucida  uniquely enabled us to reconstruct and analyze intact arteriolar networks in 3 dimensions with micrometer resolution over distances spanning millimeters to centimeters.”

When quantifying segments and overall length of capillaries in Vesselucida, Dr. Jacobsen and her team binned the data based on vessel diameter to consider changes in vasculature of varying sizes. This revealed injury and recovery-related morphological changes in capillaries (five to ten micrometers in diameter), but not in arterioles and venules.

This study is unique in demonstrating significant microvasculature damage and repair in a model of acute injury using thick tissue sections. In previous studies, thin sections have been used selectively to show cross-sections of capillaries with a measurement bias based on their proximity to muscle-cell intersects (Jacobsen, et. al. 2021).

This work demonstrates the unique power of using Vesselucida to trace capillaries and other vessels in thick sections, while avoiding the bias inherent in thin section measurements, to more accurately characterize vascular networks.

Jacobsen NL, Norton CE, Shaw RL, Cornelison D, Segal SS. Myofibre injury induces capillary disruption and regeneration of disorganized microvascular networks. J Physiol. 2021 Nov 11. doi: 10.1113/JP282292.

The post Vesselucida Helps Researchers Quantify Post-Injury Capillary Damage and Regeneration appeared first on MBF Bioscience.