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SWANSEA UNIVERSITY
Flow Cytometry Laboratory Report
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Flow Cytometry Laboratory Report
Introduction
Flow cytometry entails an indispensable tool for the chemical analysis and physical traits
of cells passing through a laser beam in medical and research setups. The technique is a method
for the measurement of light scatter and fluorescence intensity (Fleisher & Oliveira, 2019). It
quantitatively allows one to perform an individual cell detailed analysis in a short time and is used
from the diagnosis of diseases such as leukemia to assessment and research of the immune
response. This kind of technique uses fluorescent markers to stain cells that will be used in binding
some of its cell components. This allows the identification and quantification of a particular
population of cells within a sample. This is where the real value of flow cytometry, be it for clinical
diagnostics or cellular biology research, comes into play. It provides information that influences
treatment regimens and is influential in the in-depth study of complex biological systems.
Flow cytometry is a very versatile analytical tool, used in applications as diverse as
immunology to pathology, and from marine biology to the determination of the physical or
chemical characteristics of a cell or particle (Lian et al., 2019). This technology determines the
amount of light scatter and fluorescence given off by cells while passing through a beam of laser,
accounting for fast and in detail characterization of cell populations. It can assesses several
parameters quickly for each cell; hence, its great value in clinical diagnostics, most essentially for
immunodeficiencies and blood cancers, besides organ transplant monitoring. Hence, flow
cytometry remains an important tool in research laboratory work, generic applications being in
protein engineering, besides its routine use in drug discovery. Therefore, it is able to be very detailed
studied the behavior and function of cells, such as cell cycle research, apoptosis, in the meantime.
Moreover, it is beneficial in the advanced study of regenerative medicine since the accurate
quantification and sorting of stem cells and progenitor cells can be realized. The role of flow
cytometry, therefore, is not only important but actually indispensable since these are utilized in
both clinical and research settings, hence lead to advancing medical science and better patient care.
Figure 1: ACEA © NovoCyte TM flow cytometer.
The diagram shows that as the cells pass a nozzle into suspension, they are aligned one after the
other. The cell then individually intercepts the laser, and the light scatter gives information on cell
size, granularity, and fluorescence. These detectors capture the information, and hence detailed
analysis is done regarding the characteristics of the cell. This setup, for that reason, is very
important in the identification and categorization of cells, taking into consideration the physical
and biological properties exhibited by them.
Methods
The experiment of flow cytometry looked at different cell populations of blood that were
labeled with fluorescently conjugated antibodies, and the experiment was well designed, carrying
out with the required precision. For example, blood samples are first prepared by the addition of
specified antibodies in order to distinctly identify and quantify the various kinds of cells that are
contained within it. Each antibody targets one kind of antigen found at the surface of the cell and
will be conjugated to a different fluorochrome, allowing a multiparametric analysis of the studied
cell populations. It is a very critical preparation step that forms the base for the correct and clear
identification of the phenotypic markers of the cells. The following cocktail of antibodies was
added to the subsequent cell suspension samples: CD3 (PE), CD4 (FITC), and CD8 (Pacific Blue),
and CD45 (APC). All these markers were included for the fact that they belong to the subset of
lymphocytes, which is the most crucial study subject. This offers the discrimination possibility of
the different subpopulations of T-cells and is therefore of central and very high importance to
immunophenotyping, both in clinical diagnostics and research.
Following the preparation of the sample, there is a series of meticulous steps that work
towards increasing the efficiency and precision of the result. Thereafter, the same blood samples
were exposed to the fluorescently labeled antibodies, which were then subjected to a series of
washes and centrifugations. Mainly, the aim of taking away any unbound antibodies that may lead
to staining without specificity and thus give false results in cell analysis, blocking was done. After
blocking, the cells with antibodies specifically bound to them were fixed. Fixation ensures that the
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cellular structure is retained and prevents washing of the antibodies previously incubated with antigens, hence stabilizing the fluorescence for clearer detection of the signal (Behbehani, 2019).
After fixation, the sample was analyzed by flow cytometry. Where cells were passed by a flow cell where the cells were interrogated by lasers during this stage, the fluorescence from bound fluorochromes was detected. The data was recorded. The flow cytometer could measure at the same time different wavelengths, making multiparametric analysis of the cell populations possible, in order to provide a complete profile of the cellular makeup in every sample.
Figure 2: Workflow parts 2-7
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US
S
Antibodies
50 μl blood
US
S
↓
50 μl blood
10 mins dark
1 ml
1x Lysis buffer
US
S
Vortex 5 seconds
15 mins dark
Centrifuge 300 x g 10 minutes
15 mins dark
Centrifuge 300 x g 10 minutes
Figure 3: Workflow parts 8 - 12
Pour off supernatant
1 mL
FACS buffer
Centrifuge
500 x g
5 minutes
Pour off supernatant
100 μl FACS buffer
Repeat
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Final steps of the experiment included data collection and analysis. In the case of the cells
that flowed through the flow cytometer, the system would get much parameter for each cell in the
way of size, granularity, and fluorescence intensity. It is of great importance for the
characterization of the different types of cells and for knowing the idea about cellular behavior in
a much-refined manner. In modern cytometers, software helps in processing the fluorescent signals
and plots the signals on different scatter plots and histograms (Manohar et al., 2021). These visual
outputs let researcher’s gate populations of their interest and perform quantitative analysis on
certain cell subsets. It gave the most detailed cell profile within each sample: the distributions and
proportions of different types of cells. This information is highly valuable not just for diagnostic
pathology but also for use in biomedical research. This is a detailed and systematic approach in
order to safeguard both the integrity and reproducibility of the data; key elements in reaching a
scientifically valid conclusion.
Results
After processing, the samples were analyzed for lymphocyte subpopulations, mainly identified by
the use of specific markers: CD3, CD4, CD8, and CD45. This enabled systematic characterization
of the respective cells, hence giving much information on their distribution and proportions.
Attached are Figures e9 and e10, which detail the flow cytometric data obtained in this experiment.
Figure e9: Identification of Lymphocytes and T-Cell Subsets (Sample e9)
The gating strategy in Figure e9 identifies and distinguishes lymphocytes and their subsets. In the
top row, the left panel (FSC vs SSC) shows a scatter plot of Forward Scatter (FSC) vs Side Scatter (SSC), where the pink gate identifies the lymphocytes based on their light-scattering properties (23.01%). The middle panel (SSC-A vs FSC-A) highlights singlets (10.72%), indicating single
cells within the lymphocyte population. The right panel (CD45- APC-H vs SSC-H) shows CD45
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positivity, allowing for the identification of leukocytes within the singlets gate (0.00% in e9). In
the bottom row, the left panel (CD3-PE-H vs SSC-H) shows T cells (CD3+ cells) gated within the
lymphocytes, making up 75.82% of the total. The middle and right panels (CD8 vs CD4) identify
T-cell subsets: CD8-CD4+ (Helper T cells) comprise 49.58% of the total T cells, CD8+CD4-
(Cytotoxic T cells) make up 28.57%, while CD8-CD4- (21.85%) and CD8+CD4+ (0.00%)
represent the double-negative and double-positive populations, respectively.
Figure e10: Identification of Lymphocytes and T-Cell Subsets (Sample e10)
The gating strategy in figure E10 selects lymphocytes and their subsets. The pink gate (20.84%), in the left panel at the top row (FSC vs SSC), is a scatter plot of Forward Scatter (FSC) vs Side Scatter (SSC) that defines the lymphocytes in light scattering property among the total population. The central panel (SSC-A vs FSC-A) shows singlets (24.42%), indicating single cells within the lymphocyte population. The right panel (CD45-APC-H vs SSC-H) shows CD45-positivity, representing leukocytes, within the singlets gate (10.72% in e10). In the bottom row, the left panel shows T cells 75.92%
(CD3-PE-H vs SSC-H) shows the T cells (CD3+ cells) gated within the lymphocytes, with a
percentage totaling 75.82%. The mean percentages of helper T cells, cytotoxic T cells, double-
negative, and double-positive populations are 49.58%, 28.57%, 21.85%, and 0.00%.
Discussion
Flow cytometry of the two samples, e9 and e10, which were analyzed, revealed the lymphocyte
subpopulations present and the distribution of the subpopulations. Effective gating for the
lymphocytes was through properties of Forward Scatter (FSC) and Side Scatter (SSC) in the two
samples, and they were at percentages of 23.01% in e9 and 20.84% in e10. Further, gating for
singlets isolated single cells at 10.72% in e9 and 24.42% in e10. T-cells (CD3+) formed 75.82%
of the lymphocyte population in both samples. The study showed that T-lymphocyte subsets were
49.58% Helper T-cells (CD4+CD8-) and 28.57% Cytotoxic T-cells. Other remaining populations
were Double-negative T cells (CD8-CD4-), which constituted 21.85%, and Double-positive T cells
(CD8+CD4+), which were 0.00%. The results indicated that all the lymphocyte gating percentages
were within the normal range, except for the difference found at CD8 vs. CD4+, and this might
arise from sample preparation differences. Of clinical significance, the relative abundance of
Helper and Cytotoxic T cells may play an important role in the diagnosis of immune-related
disorders, autoimmune diseases, and blood cancers. Furthermore, the absence of Double-positive
T cells correlates with the normal features of peripheral blood. This represents flow cytometry as
a reliable diagnostic tool for the immunophenotypic diagnosis of immune functionality.
The practical session has, therefore, brought out the importance of flow cytometry in
clinical diagnostic tests and therapeutic monitoring, more so in the area of recognizing and
analyzing abnormality in cell populations (Loken et al., 2019). The benefit of pointing out certain
subpopulations of lymphocytes is the advantage of diagnosing several diseases that may start from
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immunological disorders up to malignant hematological diseases. The derived data can be very
elaborate and, therefore, assist in deciding about clinical processes proceeding with interventions
or monitoring the progression of the disease and reaction to treatment. Further, in clinical
applications, accuracy would be of utmost importance since early detection of diseases would
ensure more efficient treatment (Haleem et al., 2021). Further, the wide area with which flow
cytometry can be applied in many other scenarios in medicine and research gives us a hint that it
is of high value in biomedicine.
Despite the successful application of flow cytometry in our experiment, several limitations
impact the reliability and interpretation of the results. One significant limitation is the potential
degradation of samples if not processed in a timely manner, which can affect the stability and
visibility of the fluorescent markers crucial for accurate analysis (Chen et al., 2021). This
sensitivity of the procedure to quality samples demands absolute adherence to sample handling
protocols, which will assure the integrity of cells and visibility of markers. In addition, the use of
quality antibodies highly determines the accuracy of cell identification. Low-quality or improperly
stored antibodies can result in non-specific binding, which may give rise to cell counts that are
incorrect and eventually to an erroneous interpretation of cellular composition. This fact can never
be overemphasized; hence, it is extremely important to assure valid results through strict quality
control and the employment of reagents of the highest quality. Moreover, dependence on advanced
instrumentation and skilled technical operation points clearly to how standard training and
equipment calibration needs to be done. This actually raises the variability point, with the different
technical deviations that could happen and would certainly impact the reproducibility of
experiments, or their accuracy.
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In conclusion, the practical part was effective enough to demonstrate excellent
opportunities of flow cytometry in detail cellular analysis and its application in clinical diagnostics.
Although certain limitations may be reported, the biggest part of the results was on track with the
expectations and shows reliability and usability for this powerful diagnostic tool.
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References
Behbehani, G. K. (2019). Immunophenotyping by mass cytometry. Immunophenotyping:
Methods and Protocols, 31-51.
Chen, H., Shi, M., Ma, R., & Zhang, M. (2021). Advances in fingermark age determination techniques. Analyst, 146(1), 33-47.
Fleisher, T. A., & Oliveira, J. B. (2019). Flow cytometry. In Clinical immunology (pp. 1239-1251). Elsevier.
Haleem, A., Javaid, M., Singh, R. P., Suman, R., & Rab, S. (2021). Biosensors applications in medical field: A brief review. Sensors International, 2, 100100.
Lian, H., He, S., Chen, C., & Yan, X. (2019). Flow cytometric analysis of nanoscale biological particles and organelles. Annual Review of Analytical Chemistry, 12, 389-409.
Loken, M. R., Brodersen, L. E., & Wells, D. A. (2019). Monitoring AML response using “difference from normal” flow cytometry. Minimal Residual Disease Testing: Current Innovations and Future Directions, 101-137.
Manohar, S. M., Shah, P., & Nair, A. (2021). Flow cytometry: Principles, applications and recent advances. Bioanalysis, 13(3), 181-198.