BioLegend Web Updates
Blog - Using Flow Cytometry in Neuroscience Research

Sample Preparation

One of the initial hurdles in establishing flow cytometry applications in neuroscience was preparing suitable samples. When using whole brain tissue, generation of single cell suspensions is possible through a variety of methods.

Mechanical Dissociation

First, tissues need to be mechanically dissociated into small fragments using a razor blade prior to processing.

Enzymatic Digestion

Enzymes such as Trypsin, Accutase®, or Papain can be used to digest whole brain tissue samples.
Density Gradient

Percoll® or sucrose gradients help to remove myelin from the sample. For this, we use a 70/37/30% Percoll® gradient.

Cell Markers

Flow cytometry in neuroscience is useful when you need to study or isolate a homogenous population from a heterogeneous sample, such as primary cell suspensions or in vitro differentiation of neural stem cell cultures. Identification of cell type-specific markers allows for detection or isolation of these cells through magnetic cell separation or cell sorting. Cell type-specific markers have been identified for a variety of neural cell types including astrocytes, microglia, and oligodendrocytes.



Check out these references using flow cytometry to identify neural cell types:

Organelle Markers & Function

Altered organelle morphology and health is involved in numerous neurodegenerative diseases. For example, mitochondrial dysfunction and accumulation of reactive oxygen species is common in Alzheimer's and Parkinson's disease. When performing flow cytometry, in conjunction with cell type-specific markers, you can include a cell-permeant dye like MitoSpy™ as an indicator of cell health and mitochondrial function.

Similarly, lysosomal proteins help clear aggregated proteins associated with disease. Antibodies against lysosome markers like LAMP-1 or LAMP-2 can be used in flow cytometry to compare lysosome function in normal and disease states.

Find your antibodies for Neuroscience and Flow Cytometry at:
Contributed by Kelsey Swartz, PhD.

Read more from our blog...

Video - FFPE Tissue Slide Preparation and Processing
This content requires opt in of marketing cookies.
Change your cookie preferences

Watch more videos...

Video - Immunofluorescence Tissue Staining
This content requires opt in of marketing cookies.
Change your cookie preferences

Watch more videos...

Video - Chromogenic Tissue Staining using DAB Detection System
This content requires opt in of marketing cookies.
Change your cookie preferences

Watch more videos...

Video - Frozen Tissue Slide Preparation and Processing
This content requires opt in of marketing cookies.
Change your cookie preferences

Watch more videos...

Blog - Correlating Different Immunoassay Concentrations
There are a variety of immunoassays that can help determine the concentration of a particular target. The basic principle relies on the use of antibodies to bind their intended analyte. This interaction is then quantified through the use of a reporting signal.

Enzyme-linked Immunosorbent assays (ELISA) tend to be the most popular immunoassay. This setup can include, but is not limited to, the use of a single antibody, often referred to as a direct ELISA, or an antibody pair that includes both a capture and detection antibody. The use of an antibody pair is referred to as a sandwich ELISA and this method is utilized by most of the ELISA kits we offer. There are also several different labels that can be conjugated to the detection antibody to provide the reporting signal. The most popular technique uses streptavidin and HRP, which use chemiluminescence as the reporter signal. However, other assays such as our LEGENDplex™ kit rely on the excitation of a specific fluorophore as the reporting signal.

Given the variety of assays that can be developed, our team commonly addresses why two separate immunoassays may arrive at different conclusions for concentrations on the same sample. This type of discrepancy isn't uncommon for the following reasons:

Scrubs, National Broadcasting Company.

1) Differing Antibody Pairs

It is likely the antibody pairs in different kits will use distinct clones, meaning the epitope and binding kinetics of the antibodies will be different in each assay. This can have a significant impact on the reporting signal.

2) Differing standards

Vendor A and vendor B may have produced standards using different expression systems. In addition, not all recombinants are quality tested for immunoassays. Unless the vendor explicitly states the recombinants are tested with the immunoassay's detection and capture antibody, you should avoid using them in the immunoassay. This is one major reason we do not recommend using incompatible recombinants as standards in pre-established kits.

3) The Complexity of the Experimental Samples

Differing sample types can vary in complexity. In general, serum/plasma contains other components that could influence how the antibody pair binds. It is also possible for the sample to contain endogenous binding partners, which can also affect the overall concentration.

Even though two assays may determine differencing concentrations for the same sample, the comparison between these two assays is an easy process, provided the concentrations fall within the linear range of the standards.

Austin Powers: International Man of Mystery, New Line Productions.

For example, below on the left there are two standard curves for IFN-γ. Although both of these curves seem similar, we would only recommend you correlate the concentrations that fall into the 9.77 to 625 pg/ml range for IFN-γ, as the other portions of the curve are not linear.

When performing a multiplex assay, the linear portion of the standard curve will need to be determined individually for each analyte. As an additional example, I have also included IFN-β curves generated from the same assay (below on the right), but with this data set it would be best to only correlate the concentrations that fall between 39.06 and 625 pg/ml for IFN-β.
This process only works when you are deriving concentrations from the linear portion of the standard curves. If the data does not fit well with a linear regression model, a more sophisticated curve-fitting model may be necessary.

Office Space, 20th Century Fox.
Once you have determined which values are acceptable to run through a linear regression, you simply plot the concentrations obtained by each assay against each other and generate a linear regression line. As an example, I have included the concentrations (pg/ml) for IFN-γ detected with our Mouse Inflammation panel and Mouse Anti-Virus panel. These concentrations were determined through quantifying the same experimental samples using both assays. A linear regression line was generated that fits the concentrations that fall between 9.77 pg/ml and 625 pg/ml.

From this linear equation, you can then plug in the values from one assay to determine what the equivalent corresponding concentrations are from the other assay using simple algebra. This works best when both assays you are comparing have broad linear ranges. It is also important to obtain a high coefficient of determination (R2) value to ensure the concentrations fit the linear regression line adequately.

Given the diversity of immunoassays, it is important to understand the concentration that is being determined is ultimately correlated to the standards used in that particular kit. Since each vendor has their own unique mechanism for producing and detecting those standards, it should not be a surprise that different kits determine two distinct concentrations. The use of a linear regression, as described above, can help you equate concentrations from one kit to another.

Office Space, 20th Century Fox.
Contributed by Sean Cosgriff.

Read more from our blog...

Video - Antibody Manufacturing Documentary
This content requires opt in of marketing cookies.
Change your cookie preferences

Watch more videos...

Blog - BioLegend 2018: A Year in Review
Regarding TotalSeq™, Buyer is solely responsible for determining whether Buyer has all intellectual property rights that are necessary for Buyer’s intended uses of the BioLegend TotalSeq™ products. For example, for any technology platform Buyer uses with TotalSeq™, it is Buyer’s sole responsibility to determine whether it has all necessary third party intellectual property rights to use that platform and TotalSeq™ with that platform.

Read more from our blog...

Blog - Metabolites and the Immune System

What’s a Metabolite?
Metabolites are compounds that cells utilize to perform a variety of functions. By definition, they are small molecules (less than 1 kDa in size) with a variety of functions, including energy fuel, structural support, catalytic activity, and can exhibit either stimulatory or inhibitory effects on enzymes or other organisms.

Metabolomics, or the study of the metabolome, is a crucial field of study when examining cellular behavior. Shifts in cellular phenotype are first reflected within the metabolic processes of cells. Many scientists traditionally examine differences in proteomes/genomes/transcriptomes to quantitate changes in phenotype, but these changes are often seen first within the metabolome when compared to protein or gene expression. Metabolomics-based studies have the capability to give a more direct and immediate picture of the current cellular phenotype, but were not a feasible option until recently.

Comic by Randall Munroe
Metabolism at the cellular level is a relatively understudied field compared to other –omics fields, but it is growing rapidly. The introduction of new comprehensive metabolomic screening platforms have allowed scientists to investigate the overall metabolic process of cells in an unbiased way, and this has lead to a variety of discoveries on how many cells interact with a variety metabolites. In today’s blog post, we will focus on metabolites that have been found to interact with the immune system.

Metabolic Pathways & Cellular Phenotypes
Macrophages have been a popular target for metabolomics investigations, due to their phagocytic activity and dual role as either pro-inflammatory (M1) or anti-inflammatory (M2) cells. Recent studies have shown the metabolic profiles of M1 and M2 macrophages are highly distinct from one another, with M1 macrophages exhibiting high levels of glycolysis and pentose phosphate pathway activity, altered Krebs cycle activity, and increased fatty acid synthesis. On the other hand, M2 macrophages often display high levels of fatty acid oxidation and oxidative phosphorylation [1]. This discovery in particular is interesting because it has been historically established that high levels of lipid degradation and increased lipid metabolism are linked to several chronic inflammatory diseases, including SLE, RA and diabetes [2].

Mac the Macrophage from the Guardians of Biology
In addition to examining M1 and M2 phenotypes, cellular metabolic profiles of T cells have also been investigated to examine the differences between effector and regulatory CD4+ T cells. Effector T cells have an increased amount of Glut1 expression, which indicates an increase in glucose metabolism, whereas regulatory T cells rely more on lipid metabolism for their energy needs when compared to naïve T cells [3].

Investigating the energy cycles of specific cellular phenotypes has become a popular topic of research, but why these cells prefer certain energy-generating metabolic pathways is unclear. Future studies investigating the advantages and downstream effects of these pathways are needed.

Signaling Capabilities of Specific Metabolites

In addition to investigating overall metabolic pathways, several studies have focused on the signaling effects of specific metabolites and how they can affect cellular behavior.

Some metabolites have been shown to have immediate inflammation-related effects, but how they can trigger inflammatory pathways can vary. Palmitate, a common by-product of saturated fatty acid degradation, can activate TLR2 by promoting TLR2 to dimerize with TLR1 [4]. Conversely, polyunsaturated fatty acids such as DHA have been historically shown to have an anti-inflammatory effect, but the mechanism behind this activity is much more complex. [5]
Succinate, a Krebs cycle metabolite, can perform a variety of functions outside of the energy cycle activity, including increasing IL-1β production by stabilizing HIF-1α and stimulating dendritic cells by interacting with succinate receptor 1 (SUCNR1). Succinate can also act as a post-translational modification and alter gene expression, although the downstream effects of this modification are unclear [6].

Itaconic acid, another Krebs cycle metabolite, has been well-established as an anti-microbial metabolite that can inhibit bacterial isocitrate lyase to prevent bacterial growth, but it has also been shown to inhibit endogenous succinate dehydrogenase and can contribute to succinate accumulation. While this build-up of succinate can trigger pro-inflammatory responses by stimulating dendritic cells, itaconic acid itself is a powerful anti-inflammatory metabolite that activates Nrf2 and limits the type 1 interferon response [7].

Cartoon by Scott Metzger
In conclusion, metabolite-focused studies have allowed researchers to explore cell signaling and phenotypes in a new light, and further studies are needed to investigate the mechanism of action of these metabolites. Interested to find out how BioLegend can help your metabolomics and immunology based studies? Email us at to learn more!



  1. Reprogramming mitochondrial metabolism in macrophages as an anti-inflammatory signal
  2. Inflammation and metabolic disorders
  3. Cutting Edge: Distinct Glycolytic and Lipid Oxidative Metabolic Programs are Essential for Effector and Regulatory CD4+ T Cell Subsets
  4. Mechanisms for the activation of Toll-like receptor 2/4 by saturated fatty acids and inhibition by docosahexaenoic acid
  5. Omega‐3 polyunsaturated fatty acids and inflammatory processes: nutrition or pharmacology?
  6. Succinate: a metabolic signal in inflammation
  7. Itaconate is an anti-inflammatory metabolite that activates Nrf2 via alkylation of KEAP1
Contributed by Samantha Stanley, PhD.

Read more from our blog...

Blog - The Gut / Brain Connection
Art Credit: Debra Solomon
Trillions of microorganisms, collectively known as the human microbiota, colonize our gut, skin, and other tissues. This arrangement has proven largely beneficial, as these organisms can aid digestion and nutrient absorption, protect against pathogens, and even shape the development of our nascent immune system (1). However, mounting evidence suggests alterations in the microbiota play a role in the etiopathology of disease. In a follow up to our recent blog post, today we explore the emerging role of the human microbiota in the development and progression of neurological disorders.

The Bugs in Your Gut

The gut microbiota is generally thought to originate at birth. The composition of a newborn’s fecal flora is similar to that of the mother (depending on method of delivery), but is largely composed of bacteria from Actinobacteria and Proteobacteria phyla. The microbiota develops until about 2.5 years of age, at which time the function, diversity, and composition resembles that of an adult (1,2). Strains from Bacteroidetes and Firmicutes are most common, followed by Actinobacteria, Fusobacteria, Spriochaetes, Verrucomicrobia, and Lentisphaerae (4). Once a person reaches adulthood, their individual microbiota is somewhat stable, but can be altered by factors such as diet, antibiotics, and advancing age (1-3).

Importantly, the gut microbiota can vary significantly between individuals (4). So with all this talk of composition, diversity, and stability, what constitutes a “healthy” versus an “unhealthy” flora? The answer, disappointingly, is that the scientific community does not yet know. Despite this, mounting evidence suggests that deviation from the microbial “norm” (bacterial dysbiosis) is associated with the pathogenesis of certain neurological diseases.

Bacteria and Neurodegeneration

As we touched on in a previous blog post, the autoimmune disease multiple sclerosis (MS) is associated with intestinal dysbiosis. Intriguing evidence for the connection of gut commensals with MS comes from experiments conducted in experimental autoimmune encephalomyelitis (EAE) mouse models of MS. Germ-free mice were found to be resistant to EAE, unless they received a fecal transplant from mice with a normal gut flora (5). Multiple studies to date in humans have noted differences in the abundance of several bacterial genera when comparing MS patients with healthy controls, such as reductions in Lactobacillus, Clostridium, Bacteroides, and Haemophilus (6). Protein misfolding and aggregation is a hallmark of many neurodegenerative disorders such as Alzheimer’s disease (AD) and Parkinson’s disease (PD). These proteins aggregates can induce neuroinflammation and spread cell-to-cell, similar to infectious spread of prion protein characteristic of prion disease (5). The presence of amyloid-β (Aβ) aggregates and hyperphosphorylated tau are hallmarks of AD, while α-synuclein deposits known as Lewy bodies are found in PD. Coinciding with data from EAE models, germ-free α-synuclein overexpressing mice demonstrated significantly reduced motor dysfunction and neuroinflammation (5). Neuroinflammation and Aβ plaque deposition was similarly lowered in transgenic mouse AD models undergoing long-term antibiotic treatment (10).

The Connection
So how does the microbiota influence the development of these diseases? It is now known that microorganisms can produce substances that cause distal neurophysiological changes via circulation. In the case of MS, Clostridium and Bacteroides are known producers of short chain fatty acids (SCFA) and capsular polysaccharide A (PSA). SCFA fermentation products and PSA in turn promote the accumulation of Foxp3+ regulatory T cells, which have been shown to suppress neuro-inflammation in the EAE model (6,7). Increased abundance in MS patient stool samples of Methanobrevibacter and Akkermansia, the latter of which produces SCFA through the degradation of mucins, was positively correlated with upregulation of proinflammatory genes in circulating T cells and monocytes (6,8). Most recently, researchers showed metabolites of dietary tryptophan could hamper the pathogenic activities of microglia and astrocytes in murine EAE (9).

Art Credit: Public Domain

Evidence suggests that the microbiota may also promote neurodegeneration by promoting the formation of protein aggregates or by enhancing the inflammatory response to them. Multiple genera present in the microbiota produce fibers that share structural similarities with misfolded proteins. A theory holds that curli or other bacterial fibers may “cross seed” or enhance the nucleation of Aβ or α-synuclein (11). Rats exposed to bacteria that produced curli demonstrated increased production of α-synuclein in the brain and gut (12). Fibers produced by gut bacteria may also prime the immune system to mount an enhanced inflammatory response to neuronal plaques, aggravating cognitive dysfunction associated with disease. In support of this, heightened levels of neural inflammation characterized by microgliosis, astrogliosis, and increased expression of TNF, TLR2, and IL-6 was found in rats exposed to curli-expressing bacteria (12). 


With the prevalence of neurodegenerative disorders increasing due in part to lengthened lifespans, new therapies are desperately needed. Much of the evidence linking gut commensals with neurological disease remains correlative. The possibility exists that one day the microbiome may be utilized for its prognostic value, or even targeted as a treatment modality.


  1. Thursby E. and Juge N. 2017. Biochem J. 474(11): 1823-1838.
  2. Koenig J.E., et al. 2011. Proc Natl Acad Sci USA. 108(Suppl 1): 4578-4585.
  3. Dethlefsen L. and Relman D.A. 2011. Proc Natl Acad Sci USA. 108(Suppl 1): 4554-4561.
  4. Carding S., et al. 2015. Microb Ecol Health Dis. 26: 26191.
  5. Tremlett H., et al. 2017. Ann Neurol. 81: 369-382.
  6. Ochoa-Reparaz J., et al. 2017. Ann Transl Med. 5(6): 145.
  7. Wang Y., et al. 2014. Gut Microbes. 5(4): 552-61.
  8. Jangi S., et al. 2016. Nat Commun. 7: 12015.
  9. Rothhammer V., et al. 2018. Nature. 557: 724-728.
  10. Minter M.R., et al. 2016. Sci Rep. 6: 30028.
  11. Friedland R.P. and Chapman M.R. 2014. PLoS Pathog. 13(12): e1006654.
  12. Chen S.G., et al. 2016. Sci Rep. 6: 34477.
Contributed by Christopher Dougher, PhD.

Read more from our blog...

Forgot your password? Reset Password
Request an Account