CD45 Flow Cytometry: Analyzing Immune Cells

CD45 is the single most widely used marker in flow cytometry for identifying and separating immune cells from everything else in a blood or bone marrow sample. Present on virtually all cells of hematopoietic origin, CD45 acts as a universal flag for white blood cells, and the intensity with which different cell types express it provides a natural sorting system. Combined with a second measurement called side scatter, CD45 forms the backbone of a gating strategy that has largely replaced older, less reliable methods for analyzing immune cell populations in both routine clinical work and research.

What CD45 Actually Does on Immune Cells

CD45, sometimes called the leukocyte common antigen, is a protein that sits on the surface of white blood cells and works as an enzyme. Specifically, it removes phosphate groups from other signaling proteins inside the cell, which sounds arcane but has a direct practical consequence: it regulates whether immune cells activate when they encounter something. In T cells, CD45 acts on members of the Src family of signaling proteins, and this activity is required for the T cell antigen receptor to function properly.¹ In B cells, experiments using cells engineered to lack CD45 have shown that the protein controls how sensitive a B cell is to a given stimulus, essentially setting the threshold for activation.²

The protein is ancient. A version of CD45 has been identified in the Pacific hagfish, a jawless fish that diverged from the lineage leading to humans hundreds of millions of years ago. The thinking is that CD45 originally served a more general signaling role and was later co-opted to regulate the antigen receptors found in jawed vertebrates.³ For flow cytometry purposes, what matters most is that CD45 is found on essentially every white blood cell but not on red blood cells, platelets, or most non-blood cells. That makes it an ideal starting point for any analysis that needs to separate immune cells from the background noise of a sample.

Why CD45 Versus Side Scatter Replaced the Old Gating Approach

Before CD45-based gating became standard, flow cytometry labs identified cell populations using forward scatter and side scatter alone. Forward scatter roughly correlates with cell size, and side scatter reflects the internal complexity or granularity of a cell. The problem is that different cell types overlap considerably in these two dimensions. Monocytes and large lymphocytes can land in similar positions, and leukemic blast cells often sit right on top of normal populations, making them hard to isolate cleanly.

Plotting CD45 fluorescence against side scatter solves much of this. Different white blood cell populations express CD45 at reliably different levels: lymphocytes express it brightly, monocytes at an intermediate level, and granulocytes more dimly, while also having high side scatter because of their granular interiors. When labs compared the two approaches head-to-head for identifying lymphocytes, the CD45/side scatter gate recovered a similar number of lymphocytes but contained far fewer contaminating cells. Measurements taken from the CD45 gate were also more reproducible, with less variability between replicate runs, confirming that most of the noise in the old method came from non-lymphocytes slipping into the gate.⁴

The improvement is even more dramatic when analyzing leukemia samples. In a study of 74 cases of acute myeloid leukemia, blast cell percentages determined by CD45/side scatter gating correlated much more closely with microscopy-based blast counts than the old forward scatter/side scatter method did. Leukemic blasts typically express CD45 at lower levels than normal lymphocytes and monocytes, which gives them a distinct position on the plot. The conventional scatter-based approach missed this separation because the physical properties of blasts overlap heavily with those of normal cells.⁵

How Different Immune Cells Show Up on a CD45 Plot

Not all immune cells wear the same amount of CD45. Among lymphocyte subtypes, the differences are measurable and consistent. A quantitative analysis found that natural killer T cells carry the highest density of CD45, followed closely by cytotoxic T cells, then natural killer cells, helper T cells, and finally B cells with the lowest expression among lymphocytes.⁶ These differences are not typically large enough to gate individual subtypes using CD45 alone, which is why panels include additional markers like CD3, CD4, CD8, CD19, and CD56. But knowing the expected CD45 brightness for each population helps analysts spot when something looks abnormal.

This matters particularly in clinical immunophenotyping for suspected immune deficiency. Laboratories routinely use CD45 as the anchor marker while measuring B cells (CD19+), total T cells (CD3+), helper T cells (CD3+ CD4+), cytotoxic T cells (CD3+ CD8+), and natural killer cells (CD3− CD16+ and/or CD56+), reporting both percentages and absolute counts per unit volume of blood. The CD45 gate defines the total leukocyte population from which all these subsets are calculated, so its accuracy cascades through every downstream number.

CD45 expression can also change in disease. In COVID-19 patients, granulocytes and lymphocytes showed higher-than-normal CD45 expression, while monocytes showed significantly lower expression compared to healthy controls.⁷ Shifts like these could alter how cells appear on a standard gating plot, potentially confusing automated analysis if the software expects healthy-range expression. Experienced operators keep this possibility in mind, especially when gating samples from acutely ill patients.

CD45 Isoforms and What They Reveal About T Cell History

CD45 is not a single uniform molecule. The gene that encodes it can be spliced in different ways, producing several isoforms. The two most clinically relevant are CD45RA and CD45RO. In a simplified version of the story, naive T cells (those that have never encountered their specific antigen) express CD45RA, while memory T cells (those that have responded to infection and then persisted) express CD45RO. This distinction is heavily used in transplant monitoring and vaccine research to track how the immune system rebuilds or responds over time.

The reality is more complex. Work tracking CD8 T cells specific for yellow fever virus over more than a decade showed that these cells do not simply flip from CD45RA to CD45RO and stay there. During the initial immune response, the cells switch to CD45RO as expected. But within about four to six weeks, the virus-specific cells revert to CD45RA and remain CD45RA-positive for over ten years. The full trajectory moves through naive (CD45RA+ CCR7+) to effector memory (CD45RO+ CCR7−) to a terminally differentiated effector memory state re-expressing RA (CD45RA+ CCR7−) and eventually toward a stem-cell memory phenotype (CD45RA+ CCR7+).⁸ The practical takeaway is that a cell expressing CD45RA is not necessarily naive. Especially among CD8 T cells, CD45RA can mark a highly experienced, long-lived memory population.

This nuance shows up in transplant recovery data. After stem cell transplant, the ratio of CD45RA to CD45RO cells is used to track whether the new immune system is generating fresh naive T cells or simply expanding existing memory cells. A study comparing peripheral blood stem cell transplant recipients to bone marrow transplant recipients found that the peripheral blood group recovered naive T cells (CD45RA+) and helper T cells faster, while the bone marrow group showed a quicker expansion of memory T cells (CD45RO+) and cytotoxic T cells.⁹ These dynamics inform clinical decisions about which graft source to use and when a transplant recipient’s immune system can be considered functionally restored.

Leukemia Diagnosis and Tracking Residual Disease

The CD45/side scatter plot is arguably most valuable in hematologic malignancies. Leukemic blast cells express CD45 at abnormally low levels, which places them in a characteristic position on the plot: dim CD45 fluorescence and low-to-moderate side scatter. This makes them visually distinct from lymphocytes, monocytes, and granulocytes even before any additional markers are applied. One study found that morphometric analysis of the blast cluster on a CD45/side scatter plot could distinguish between acute lymphoblastic leukemia and acute myeloid leukemia based on the shape of the blast population alone, with a width-to-height ratio below 1.6 suggesting myeloid disease and above 1.6 suggesting lymphoblastic disease.¹⁰

After treatment, the same principle applies to detecting minimal residual disease, the small number of leukemic cells that survive chemotherapy and can eventually cause relapse. Using CD45 gating to define the blast region, then looking for cells with aberrant marker combinations within that region, is more sensitive than the old scatter-based approach. In one evaluation of patients with acute leukemia and high-risk myelodysplastic syndrome who had achieved complete remission, the frequency of blasts with abnormal marker expression was substantially higher when measured by CD45/side scatter gating than by the conventional method, and in some cases the CD45-based approach predicted relapse weeks before it became clinically apparent.¹¹

In the context of AML specifically, the maturation patterns visible on CD45 and related marker combinations follow a predictable sequence that experienced analysts learn to read. Normal myeloid precursors gain CD45 expression as they mature, so a blast population stuck at dim CD45 stands out. The same principle extends to erythroid and megakaryocytic precursors, where different combinations of markers like CD34, CD117, CD71, and CD105 trace defined maturation trajectories that help distinguish normal regenerating marrow from residual disease.¹²

Technical Pitfalls That Affect CD45 Results

Several practical variables can shift where cells appear on a CD45 plot and lead to inaccurate results if not controlled for.

The red blood cell lysis step, which removes red cells before analysis so they do not crowd out the white blood cells of interest, is a surprisingly large source of variability. A comparison of four commonly used lysis reagents found that one (FACS Lysing Solution) yielded significantly lower blast percentages than the other three, all tested on the same samples.¹³ This means that a lab switching lysis reagents, or a study comparing results across sites using different reagents, could see shifts in blast counts that have nothing to do with the biology. Standardizing the lysis protocol across a study or clinical network is essential for meaningful comparisons.

When working with tissue samples rather than blood, the method used to free cells from tissue presents its own challenges. Enzymatic digestion with dispase, a protease commonly used to break apart tissue, can cleave surface markers including CD4, CD8, CD69, and CD103 in a dose-dependent manner.¹⁴ A broader evaluation of 48 immune-relevant surface markers found that while collagenase had minimal effects on most targets, dispase treatment substantially reduced the detectability of the majority of markers tested, and the damage persisted for at least 24 hours even after the enzyme was washed away. High-dose dispase treatment also impaired T cell function in subsequent assays.¹⁵ CD45 itself is relatively resistant to enzymatic clipping, which is another reason it works well as the anchor marker in tissue-derived samples where other markers may be compromised.

A separate issue arises with monocytes and macrophages, which carry receptors on their surface that non-specifically grab antibodies by their tails rather than their antigen-binding ends. This can create false-positive staining for markers the cells do not actually express. The fix is straightforward: blocking with a commercial Fc receptor-blocking reagent or with high-concentration serum or immunoglobulin eliminates the problem.¹⁶ Labs that skip this step risk misidentifying monocyte populations within the CD45 gate. When studying intracellular targets alongside surface CD45, fixation and permeabilization protocols also need careful optimization to preserve both scatter properties and surface staining patterns. A combined approach using formaldehyde fixation with detergent-based permeabilization and methanol denaturation has been shown to maintain light scatter and CD45-based gating while allowing detection of intracellular phosphorylated proteins.¹⁷

CD45 in Spectral Flow Cytometry and Mass Cytometry

As flow cytometry instruments have grown more powerful, measuring dozens of parameters simultaneously, CD45 has gained new roles beyond simple gating. In spectral flow cytometry, which captures the entire emission spectrum of each fluorochrome rather than splitting light into discrete detectors, accurate reference controls are critical for the mathematical “unmixing” that assigns each signal to the correct fluorochrome. A recently described tool called CaptureBody uses a bispecific antibody that binds CD45 on one end and captures any antibody on the other, generating single-stain controls directly on immune cells rather than on artificial beads. Controls made this way matched the spectral signatures of directly stained cells with extremely high fidelity, while bead-based controls showed substantially worse agreement for several commonly used fluorochromes.¹⁸ The reason this works is precisely because CD45 is on every immune cell: the bispecific antibody can capture any other antibody onto the cell surface, creating a control that behaves optically like the real staining condition.

In mass cytometry, which uses heavy-metal isotope tags instead of fluorochromes and can measure over 40 markers at once, CD45 has been repurposed as a barcoding tool. By conjugating anti-CD45 antibodies with different metal isotopes at specific ratios, researchers can label samples from different donors or experimental conditions, pool them for staining and acquisition, and then computationally separate them afterward.¹⁹ This approach is particularly useful when working with precious clinical samples that cannot spare cells for separate staining reactions. By running multiple samples in a single tube, it also eliminates batch effects between samples, making comparisons cleaner.²⁰

The CD45 Congenic System in Mouse Research

CD45 has a parallel life in laboratory mouse research that is invisible to most clinicians but foundational to transplant immunology. Two common mouse strains carry slightly different versions of the CD45 protein, designated CD45.1 and CD45.2, which are functionally identical but distinguishable by specific antibodies. This allows researchers to transplant bone marrow or purified stem cells from a CD45.1 donor into a CD45.2 recipient (or vice versa) and then use flow cytometry to track exactly which cells came from the graft and which are host-derived.²¹

This congenic marker system has been used to answer questions that would be impossible to study in humans, such as whether the number of transplanted stem cells affects how fast the immune system rebuilds. In one set of experiments using purified hematopoietic stem cells transplanted at three different doses, flow cytometry with CD45 allele-specific antibodies showed that higher stem cell doses produced faster recovery of T cell and B cell populations in the blood and spleen.²² Other groups have used the same system to test whether co-infusing mesenchymal stromal cells alongside stem cells improves engraftment.²³

The system is not perfect. The two CD45 alleles are not entirely neutral biologically. When both allelic versions are present in the same animal (a mixed bone marrow chimera), B cell subsets from the CD45.1 donor and the CD45.2 host can be represented at different frequencies than expected, suggesting the alleles are not completely interchangeable in all contexts.²¹ Researchers who use the system are generally aware of this caveat and design controls accordingly, but it is a reminder that even a “neutral” tracking marker can introduce subtle biases into experimental results.

Tumor-Infiltrating Immune Cells and Tissue Analysis

Solid tumor immunology has pushed CD45 flow cytometry into new territory. When a tumor is dissociated into single cells, CD45 is the primary way to separate immune cells from cancer cells and stromal tissue. In colorectal cancer, for example, flow cytometry using CD45 has revealed that the fraction of tumor tissue made up of immune infiltrates varies enormously from patient to patient, ranging from as little as five percent to as much as ninety percent.²⁴ Within that CD45+ population, detailed phenotyping can identify rare subsets like gamma-delta T cells and characterize their functional state, information that informs how well the immune system is fighting the cancer and whether immunotherapy might help.

The challenge is that tissue dissociation, as noted earlier, can strip or damage the very markers researchers want to measure. CD45 tends to survive enzymatic treatment better than many other surface molecules, which reinforces its role as the starting gate. But downstream markers used to classify T cell subsets or activation states are more vulnerable, and any protocol involving dispase needs careful validation to ensure the phenotype being read from the flow cytometer reflects what was actually on the cells in the tissue, not an artifact of the preparation method.