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PAPER
Cite this: DOI: 10.1039/d3an00686g
Received 30th April 2023,
Accepted 5th September 2023
DOI: 10.1039/d3an00686g
rsc.li/analyst
Effect of pre-analytical variables on Raman and
FTIR spectral content of lymphocytes†
Jade F. Monaghan,
a,b
Daniel Cullen,
a,b
Claire Wynne,
c
Fiona M. Lyng *‡
a,b
and Aidan D. Meade *‡
a,b
The use of Fourier transform infrared (FTIR) and Raman spectroscopy (RS) for the analysis of lymphocytes
in clinical applications is increasing in the field of biomedicine. The pre-analytical phase, which is the
most vulnerable stage of the testing process, is where most errors and sample variance occur; however, it
is unclear how pre-analytical variables affect the FTIR and Raman spectra of lymphocytes. In this study,
we evaluated how pre-analytical procedures undertaken before spectroscopic analysis influence the
spectral integrity of lymphocytes purified from the peripheral blood of male volunteers (n= 3). Pre-
analytical variables investigated were associated with (i) sample preparation, (blood collection systems,
anticoagulant, needle gauges), (ii) sample storage (fresh or frozen), and (iii) sample processing (inter-oper-
ator variability, time to lymphocyte isolation). Although many of these procedural pre-analytical variables
did not alter the spectral signature of the lymphocytes, evidence of spectral effects due to the freeze–
thaw cycle, in vitro culture inter-operator variability and the time to lymphocyte isolation was observed.
Although FTIR and RS possess clinical potential, their translation into a clinical environment is impeded by
a lack of standardisation and harmonisation of protocols related to the preparation, storage, and proces-
sing of samples, which hinders uniform, accurate, and reproducible analysis. Therefore, further develop-
ment of protocols is required to successfully integrate these techniques into current clinical workflows.
1. Introduction
In recent years, RS and FTIR spectroscopies have demonstrated
their potential in translational and clinical applications such
as screening, diagnosis, monitoring, and prognosis.
1–8
This is
mainly due to these complementary techniques sharing the
same advantages, i.e., being non-destructive, label free, cost-
effective modalities for analysis for biological samples which
require minimal sample preparation. However, to exploit the
clinical potential of these bio-analytical techniques, protocols
first need to be developed to facilitate uniform and reliable
spectroscopic analysis, to ensure discovery of robust spectral
profiles with a high degree of sensitivity and specificity.
Blood is already a commonly collected biosample in a clinical
setting and is an invaluable source of information for spectral
profiling of patients.
9
Blood collection is minimally invasive, low-
cost, and provides a temporal snap-shot of the biomolecular
status of the patient.
10
Blood contains erythrocytes, leukocytes
(neutrophils, eosinophils, basophils, monocytes and lymphocytes;
B, T, NK cells) and platelets which are suspended in plasma.
11
Using density gradient centrifugation, it is possible to isolate peri-
pheral blood mononuclear cells (PBMCs) from whole blood. This
heterogeneous fraction is made up of immune cell subsets of
dendritic cells (1–2%), monocytes (10–20%), NK cells (5–20%),
and T (75–85%) and B lymphocytes (5–10%).
12
Due to recent technological and computational advance-
ments, FTIR and RS can identify and differentiate subpopu-
lations of lymphocytes and monocytes.
13,14
These techniques
have also been demonstrated to be capable of differentiating
between resting and activated lymphocytes, in addition to
monocytes and macrophages.
15–18
These proof-of-concept
studies demonstrate that both RS and FTIR are invaluable
tools for PBMC analysis. Other studies have shown that RS and
FTIR analysis of PBMCs and pure lymphocytes holds great
promise for clinical real-world applications in the analysis of
malignancies, infectious diseases, immune response, occu-
pational contaminants, drug response, radiotherapeutic
response, and for retrospective radiobiological dosimetry.
19–31
The potential of both RS and FTIR spectroscopy as a robust
clinical tool is clear; however, both types of analysis are far
†Electronic supplementary information (ESI) available. See DOI: https://doi.org/
10.1039/d3an00686g
‡Authors contributed equally.
a
School of Physics, Clinical and Optometric Sciences, Technological University
Dublin, Central Quad, City Campus, Grangegorman, D07 XT95, Ireland.
E-mail: aidan.meade@tudublin.ie, fiona.lyng@tudublin.ie
b
Radiation and Environmental Science Centre, Focas Research Institute,
Technological University Dublin, Aungier Street, D02 HW71, Ireland
c
School of Biological, Health and Sports Sciences, Technological University Dublin,
Central Quad, City Campus, Grangegorman, D07 XT95, Ireland
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from being translated into a clinic environment as currently
there are no standardised protocols relating to sample prepa-
ration, storage, and processing. The biomolecular quality and
composition of biological samples can be altered by environ-
mental or technical pre-analytical variability, analytical varia-
bility and post-analytical variability.
32
The nature of sample
processing in the pre-analytical phase offers a window of sus-
ceptibility for the introduction of sample variance and has
been deemed the most vulnerable phase of testing with up to
61.9% of laboratory errors originating at this point.
33,34
The
pre-analytical phase in this context can be defined as all pro-
cedures prior to the commencement of spectroscopic
analysis.
35
RS and FTIR datasets contain highly detailed information
relating to the biomolecular composition of a sample, varia-
bility from sample preparation approach induced at the pre-
analytical phase could possibly mask clinically important spec-
tral features and/or be mistaken for a clinically significant
finding. To the best of our knowledge, this study is the first of
its kind, as no other study in the literature explores the bio-
chemical variability in RS and FTIR of lymphocytes from mul-
tiple processing parameters at various stages of lymphocyte
sample preparation.
2. Materials and methods
2.1. Ethical approval
Informed consent was obtained from healthy volunteers and
peripheral blood was collected by venous blood sampling from
three male human donors between 28 and 35 years of age.
Therefore, for each pre-analytical variable investigated, three
peripheral blood samples were obtained and analysed. All
sample collections were carried out in accordance with the
1964 Helsinki Declaration
36
and approved by the
Technological University Dublin Research Ethics Committee
(REC number 15-32). Volunteer information was anonymised
prior to analysis.
In summary, variables investigated in this study that have
the potential to impact lymphocyte spectral information
included:
Sample preparation: (1) the Vacuette® and S-Monovette®
blood collection systems, (2) 21 and 23 gauge (G) needles, (3)
EDTA (ethylenediaminetetra-acetic acid), lithium heparin (LH)
and sodium citrate (SC) Vacuette® anticoagulant blood collec-
tion tubes. Sample storage: (1) freezing samples (frozen versus
non-frozen lymphocytes). Sample processing: (1) different
operators processing samples (inter-operator variability), and
(2) lymphocytes isolated within 1 hour or after 24 hours post-
sample collection (Fig. 1).
Unless otherwise indicated in the methodological summary
(Table 1), peripheral blood samples were collected using the
Vacuette® blood collection system (Greiner Bio-One,
Stonehouse, UK) with Vacuette® LH anticoagulant (Greiner
Bio-One), 21 gauge needle (Greiner Bio-One) and immediately
processed by the same operator. All volunteer lymphocyte
samples for each pre-analytical variable were analysed by FTIR
and RS, except for FTIR analysis of lymphocytes collected with
different needle gauges. This exception was due to the
extended elapsed time between sample fixation and recording
caused by the COVID-19 campus closure. As a result, there is a
possibility that protein degradation occurred at an accelerated
rate, which could have been misinterpreted as a spectroscopic
effect related to the investigated needle gauges.
37
For each pre-
analytical variable, 10 mL of peripheral blood was collected
from each volunteer. However, due to the availability of volun-
teers, samples were not obtained from the same three volun-
teers for each pre-analytical variable.
Fig. 1 Schematic depiction of the experimental design of each pre-analytical consideration.
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2.2. Preparation of PBMC culture
PBMC culture preparation was carried out as previously
described by Maguire et al.
29
In a 50 mL polypropylene tube
(Thermo Fisher Scientific Inc., MA, USA), 6 mL of room temp-
erature Dulbecco’s phosphate buffered saline (DPBS; Sigma
Aldrich LLC, MO, USA) and volunteer blood were mixed. In a
new 50 mL polypropylene tube, 15 mL of Histopaque-1077
(Sigma Aldrich LLC) was added and the mixture of DPBS and
peripheral blood was pipetted on top, allowing the mixture to
gently run down the side of the tube. Sample tubes were cen-
trifuged at 400gfor 30 minutes at 18 °C with deceleration set
to zero to allow the whole blood to be separated into different
fractions, i.e., density centrifugation. The PBMC layer was gently
aspirated using a 5 mL pipette (SARSTEDT, Germany) and
washed in 50 mL polypropylene tubes by adding DPBS to a total
volume to 30 mL and mixed by inversion. The sample was then
centrifuged at 400gfor 4 minutes at 18 °C with the acceleration
set to nine. The washing step was repeated twice more with 5 mL
of DPBS added to the cell pellet at each wash, bringing the total
number of washes to three. After the last wash, DPBS was
removed, and the cell pellet was resuspended in 5 mL of full
medium (RPMI + 12.5% vol/vol FBS + 2 mM L-glutamine; Sigma
Aldrich LLC). The resuspended cell pellet was transferred to a
25 cm
2
tissue culture flask (SARSTEDT) and supplemented with
2.5% vol/vol (50 μL) of phytohaemagglutinin (PAA Laboratories,
West Yorkshire, UK). PBMCs were incubated with a Thermo
Scientific Forma 320 CO
2
incubator (Thermo Fisher Scientific
Inc.) for 68–72 hours at 37 °C with 5% CO
2
. The flasks were
placed in a horizontal orientation to facilitate the separation of
lymphocytes and monocytes by plastic adherence.
2.3. Calcium fluoride (CaF
2
) slide preparation
Lymphocytes were brought into suspension by gently swirling
the tissue culture flasks. The supernatant containing the lym-
phocytes was aspirated into a new 30 mL polypropylene tube.
The tissue culture flasks were then washed with 5 mL of DPBS.
The DPBS was decanted into 30 mL polypropylene tubes
(Thermo Fisher Scientific Inc.) and the washing step was
repeated twice more. The sample tubes were centrifuged at
400gfor 5 minutes at 18 °C with the deceleration and accelera-
tion set to 9. The supernatant was decanted, and the cell pellet
was resuspended in 200 μL of 2% vol/vol paraformaldehyde
(PFA; AppliChem GmbH, Darmstadt, Germany). The cells were
fixed for 10 minutes at room temperature. From each sample
suspension, 20 μL was aliquoted onto a labelled CaF
2
slide
(Crystran Ltd, Dorset, UK), and allowed to settle and attach to
the slide for 5 minutes, after which the liquid supernatant was
removed. Cells were then washed in deionised water three
times. Finally, excess deionised water was removed, and the
sample was allowed to air dry.
2.4. Acquisition of Raman spectra
Raman spectral acquisition was carried out using a protocol
described previously.
31
Before sample analysis, room tempera-
ture was set at 18 °C for 20 minutes. RS was performed using a
Horiba Jobin Yvon LabRAM HR800 UV micro-Raman spectro-
meter (Horiba UK Ltd, Middlesex, UK), equipped with a cooled
charge-coupled device (CCD) array detector with 1024 × 256
pixels and Peltier cooled to −70 °C. The Raman spectrometer
was also equipped with a 660 nm solid-state diode laser that
directed 100 mW power at the sample. An initial calibration of
the detector used 520.8 cm
−1
of crystalline silicon, as here
pixels are assigned wavenumber values in the CCD camera.
38
A
100× objective (numerical aperture = 0.9) and a 600 line per
nm diffraction grating (centred at 1450 cm
−1
) was used. The
confocal hole and filter transmission were set to 100 μm and
100%, respectively. Spectra (n= 3) of a known standard, 1,4-bis
(2-methylstyryl)benzene (Sigma Aldrich LLC) were recorded with
a0.3secondsintegrationtimeandaveragedover3integrations
per spectrum and used for wavenumber calibration in post-pro-
cessing.
39
Singlecellspectrawererecordedusinga4×4μm
raster scan of the centre of each cell with a 20 seconds inte-
gration time and averaged over 3 integrations, along with a spec-
tral resolution of 1 cm
−1
. The Raman acquisition of the samples
was performed over the spectral range of 1800–400 cm
−1
. Cells
were visually inspected before recording to ensure mostly lym-
phocytes were included in subsequent Raman analysis. Between
25 and 35 spectra were recorded from each sample. The variation
in the number of spectra recorded per sample is due to variability
in sample quality. The CaF
2
slides, with fewer recorded lympho-
cyte spectra, experienced a higher degree of cell loss during the
final washing steps with deionized water, as outlined in Section
2.3. Spectral acquisition was carried out using LabSpec v.6
(Horiba, UK).
Table 1 A brief outline of the experimental design for pre-analytical variables: sample preparation, storage, and processing. The pre-analytical vari-
able being assessed is highlighted in bold
Pre-analytical variable
Collection
system Anticoagulant
Needle
gauge
Operator
(n)
Time to lymphocyte
isolation post-sample
collection Freezing samples
Blood collection systems Vac and SM LH 21 G 1 1 h Non-frozen
Needle gauges Vac LH 21 G and 23 G 1 1 h Non-frozen
Anticoagulant Vac LH, SC, and EDTA 21 G 1 1 h Non-frozen
Freezing samples Vac LH 21 G 1 1 h Frozen and non-frozen
Inter-operator variability Vac LH 21 G 21 h Non-frozen
Sample processing time Vac LH 21 G 1 1 h and post-24 h Non-frozen
Vac = Vacuette®, SM = S-Monovette®. A detailed summary of the experimental design of each pre-analytical variable can be found in the ESI.†
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2.5. Raman spectra pre-processing
All pre-processing of Raman spectra was performed in Matlab
version R2017b (MathWorks Inc., MA, USA) and Toolbox v.8.0
(Eigenvector Research Inc., MA, USA) using in-house algor-
ithms. All Raman spectra were wavenumber calibrated cor-
rected with recorded spectra of 1,4-bis(2-methylstyryl)benzene.
Following this, the Raman spectra were truncated to include
the region of 1795–408 cm
−1
. Baseline removal was performed
using a rubberband method
40,41
and spectra were smoothed
using a Savitzky–Golay filter (5
th
order polynomial, 15 point
window, or 14 wavenumbers). Finally, all Raman spectra were
standardised using vector normalisation. An example of the
spectral pre-processing steps carried out on lymphocyte
Raman spectra is provided in ESI Fig. 1.†
2.6. Acquisition of FTIR spectra
The FTIR spectra were collected using a PerkinElmer Spotlight
400 mid-infrared imaging system (PerkinElmer, Waltham, MA)
using a liquid-nitrogen cooled mercury cadmium (MCT) 16 × 1
linear array detector. This system is equipped with an
AutoImage microscope system operating with a 40× Cassegrain
objective. The FTIR acquisition of the samples was performed
in transmission mode, over the spectral range of
4000–800 cm
−1
and with an interferometer speed of 1.0 cm
s
−1
. An aperture size of 100 μm × 100 μm was selected as this
aperture size produced the spectra with the best signal to
noise ratio. A total of 128 scans per pixel were recorded and
averaged at a spectral resolution of 4 cm
−1
to maximise the
signal-to-noise ratio.
42
Before each sample measurement, 256
background scans per pixel were recorded in an empty space
on the CaF
2
side to remove contributions from carbon dioxide
and water vapour.
43
For each sample, 25–35 spectra were
recorded with each spectrum recorded from around 30–50 lym-
phocytes. As mentioned above, cells were visually inspected
prior to recording to ensure that mainly lymphocytes were
included in subsequent FTIR analysis. Spectral acquisition was
performed using Spectrum-IMAGE-Spotlight-400 software
v.10.5.4 (PerkinElmer, MA).
2.7. FTIR spectra pre-processing
All pre-processing of FTIR spectra was carried out in OCTAVVS
(Open Chemometrics Toolkit for Analysis and Visualisation of
Vibrational Spectroscopy Data) version 0.1.17, in Python
version 3.9.13.
44
First, atmospheric correction was performed to remove H
2
O
and CO
2
spectral contributions. Next, the distorted baseline
due to Mie scattering was corrected using the resonant Mie
scattering correction (RMieS) algorithm developed by Bassan
et al.
45
The settings selected within the RMieS algorithm
include 20 iterations, 8 orthogonal principal components, and
a matrigel reference spectrum. Following this, the FTIR spectra
were truncated to the fingerprint region from 1800–900 cm
−1
.
The spectral baseline was removed using a rubberband fitting
method.
40,41
Finally, prior to spectroscopic analysis, spectra
were standardised using vector normalisation. The second
derivative spectra were calculated using the Savitzky–Golay
algorithm (2
nd
order polynomial, 9-point window, or 32 wave-
numbers). The resulting spectra were used for multivariate
analysis. An example of the spectral pre-processing steps
carried out on lymphocyte FTIR spectra is provided in ESI
Fig. 2.†
2.8. PCA
Principal Components Analysis (PCA) is an unsupervised
dimensionality reduction technique and is used extensively in
the identification of clusters, patterns, and outliers in spectro-
scopic datasets. Here, principal components analysis (PCA)
was used for exploratory data visualisation.
As PCA is sensitive to outliers, these must first be identified
and removed before analysis is carried out. In this study,
outlier removal was approached using the popular Hotelling
T
2
test as it provides an easily interpreted visual of outliers
present within the spectroscopic datasets.
46
Normalised RS
and FTIR spectral datasets corresponding to a processing tech-
nique in each pre-analytical consideration were analysed for
outliers via Hotelling T
2
test. The Hotelling T
2
test is the multi-
variate alternative to Student’sT-test to assess the difference in
the means of the data.
47
In this study, 10 PCs were included in
the Hotelling’sT
2
model and a 95% confidence limit was
selected. Samples with a T
2
range that fell outside of this
range were removed from further analysis. Hotelling’sT
2
range
represents the sum of normalised squares, which can also be
explained as the distance of the sample from the centre to the
projection of the sample onto the number of principal com-
ponents included in the PCA model.
48
AT
2
score of 0 is
obtained when the sample is projected onto the multivariate
centre of the model.
For Raman spectra, PCA was performed on spectra acquired
over the fingerprint region from 1795–408 cm
−1
, while for
FTIR spectra, PCA was performed on the second derivative
spectra over the fingerprint region from 1800–900 cm
−1
.For
both Raman and FTIR spectra, PCA was performed with 10
principal components. There is no consensus on the number
of PCs to include in a PCA model of spectroscopic datasets.
However, lower PCs represent the most common information
in the dataset while the opposite is said for higher PCs.
Inclusion of the latter in a PCA model would involve the
measurement of noise and hence, have been excluded from
our analysis.
49
The inclusion of 10 PCs produced a model
describing X% of the variance in the data, whilst reducing its
dimensionality substantially. PCA outputs were represented by
3-D score plots and 2-D score plots. For the latter, the covari-
ance ellipse (95% confidence) was used to highlight the extent
of the variance in each dataset.
3. Results and discussion
3.1. Sample preparation
3.1.1. Blood collection systems: S-Monovette® and
Vacuette® system. As outlined earlier, in this study peripheral
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blood samples were obtained from volunteers through two
common blood collection methods: aspiration systems
(S-Monovette®) and vacuum systems (Vacuette®). The
Vacuette® collection system operates by utilising vacuum
pressure to draw blood into the blood collection tubes, while
the S-Monovette® system is equipped with both vacuum and
aspiration options. In the latter method, the speed of blood
collection can be regulated by the phlebotomist, and in the
context of this study, venipuncture blood was obtained via the
aspiration mode of the S-Monovette® system.
Lymphocyte FTIR spectra of PBMCs drawn using the
Vacuette® system were highly uniform as evidenced by the
narrow standard deviation around each mean spectrum
(Fig. 2A), and the similarly condensed dispersion in the PC
scores (Fig. 2C). In contrast, the FTIR spectra of lymphocytes
collected through the S-Monovette® blood collection system
showed substantial variability over the wavenumber range of
994–1124 cm
−1
in their mean spectra. This region of the spec-
trum is tentatively attributed to vibrations associated with gly-
cogen, collagen, C–O–C stretching (in nucleic acids and phos-
pholipids), DNA (due to PO
2
−
vibrations), symmetric stretching
of phosphate groups in phospholipids, and C–O stretching of
phosphodiester and ribose. Similarly, the PC scores for this
group depicted broad dispersion, reinforcing the variability in
the spectra in this sample, while the loadings to PC1 (Fig. 2E)
are associated with spectral regions dominated by contri-
butions from nucleic acids and proteins (from 964–1092 cm
−1
and 1518–1580 cm
−1
).
A similar trend was also observed in the mean lymphocyte
Raman spectra (Fig. 2B) for lymphocytes collected with the
S-Monovette® system. Here, Raman bands tentatively assigned
to ring breathing of tryptophan, guanine and thymine
(648–674 cm
−1
), single-bond stretching vibrations of amino
acids and polysaccharides (845–857 cm
−1
), amide III and
lipids (1233–1269 cm
−1
), amide II, ν(CvC) of carotenoids and
tryptophan (1498–1587 cm
−1
), and ν(CvO) of amide I and
CvC stretching of lipids displayed considerable variance com-
pared to similar bands for the Raman spectra of lymphocytes
collected using the Vacuette® system. The spectral variance
seen in the PC score plot (Fig. 2D) is reflective of the obser-
vations in the FTIR spectra, with spectral regions of the load-
ings (Fig. 2F) dominated by Raman signatures from amide II
β-sheet, amide I α-helix confirmation and lipids
(1626–1700 cm
−1
). Compared to the aspiration method, i.e.,
manual gentle gradual drawing of the plunger, it has pre-
viously been shown that the effect of shear forces inflicted on
Fig. 2 Mean FTIR (A) and Raman (B) spectra of lymphocytes collected with S-Monovette® or Vacuette® blood collection systems, PCA score plot
of FTIR (C) and Raman (D) spectra, PC1 loading of FTIR spectra (E) and PC2 loading of Raman spectra (F). Covariance ellipses (95% confidence limit)
in (C) and (D) are shown for each data class.
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the blood flow through the needle during the blood draw
process via a conventional vacuum system can result in an
increased rate of haemolysis in serum samples.
50,51
The spec-
tral profiles indicative of individual biochemical characteristics
were detected in our FTIR lymphocyte spectral datasets and
the S-Monovette® lymphocyte Raman spectra. This is particu-
larly evident in the PCA plots (Fig. 2C and D) of the
S-Monovette® lymphocyte spectral datasets, where a clear sep-
aration into three distinct subclusters is observed, with each
subcluster representing a healthy volunteer.
Furthermore, while Vacuette® lymphocyte Raman spectra
are suggestive of a similar effect from shear forces in PBMCs,
we did not observe an associated increase in haemolysis.
3.1.2. Needle gauge: 21-gauge and 23-gauge needle. In
phlebotomy, 21 G and 23 G needles are commonly employed
for venous blood collection, with the standard of care being
the use of a 21 G needle. The choice of needle size ultimately
depends on the suitability of the patient’s veins, and it should
be noted that as the gauge number increases, the bore dia-
meter of the needle decreases. Previous studies on the impact
of needle bore size on cellular integrity have produced conflict-
ing outcomes. Wollowitz et al.
52
reported that smaller bore
needles, such as the 23 G needle, can result in an increased
rate of haemolysis compared to the 22 G needle. Conversely,
Lippi et al.
53
evaluated the effect of haemolysis on electrolytes
in venous blood collected with 21 G, 23 G and 25 G needles
and found that samples collected with 25 G needles exhibited
haemolysis levels that could potentially compromise sample
analysis, while no such observations were made for samples
collected with 21 G or 23 G needles. The impact of different
needle gauges on lymphocyte spectral data remains unclear.
Here, we addressed this issue through the evaluation of the
Raman spectra of lymphocytes obtained through blood draws
via a 21 G or 23 G needle. The mean Raman spectra (ESI
Fig. 3†) and PCA (Fig. 3) scores plots both suggest that the
choice of needle bore (at the dimensions studied here) does
not have a substantial effect on the spectral content of
lymphocytes.
3.1.3. Anticoagulant: EDTA, lithium heparin, and sodium
citrate. Using three types of anticoagulants, i.e., EDTA, LH and
SC, peripheral blood was collected via venepuncture. These
anticoagulants are widely used in clinical settings to collect
blood samples intended for biochemical analysis. The mean
FTIR and Raman spectra of lymphocytes collected in three of
the different anticoagulant tubes are shown in ESI Fig. 4.†In
the FTIR lymphocyte PCA scores plots (Fig. 4A), no discernible
separation was observed, though there is a slightly reduced
variance in the EDTA sample. This feature is, however, not
reproduced within the PCA score plot of Raman lymphocyte
spectra (Fig. 4B), though again spectra did not differentiate on
coagulant. Other previous studies have also investigated the
effect of various tube substrates on the spectral content of
human plasma and erythrocytes. Lovergne et al.
54
highlighted
that the use of LH would be more suitable for the spectro-
scopic analysis of plasma compared to EDTA. Martin et al.
55
also found this to be true when considering erythrocyte ana-
lysis via attenuated total reflectance (ATR)-FTIR for low-range
detection and quantification of malaria parasitemia.
To our knowledge, this study is the first to assess the
impact of EDTA, LH, and SC anticoagulant on the spectral
integrity of lymphocytes. The results presented here indicate
that the choice of anticoagulant has a minimal effect on the
spectral properties of lymphocytes. This finding may be
explained by time in culture and subsequent numerous
washing steps.
3.2. Sample storage
3.2.1. Impact of freezing on lymphocyte spectral reproduci-
bility. In clinical and research settings, blood samples may not
be processed immediately for various reasons, such as trans-
portation to the testing facility, availability of personnel, and
Fig. 4 PCA score plot of FTIR (A) and Raman (B) spectra of lymphocytes
collected with EDTA, LH, and SC anticoagulant. Covariance ellipses (95%
confidence limit) in (A) and (B) are shown for each data class.
Fig. 3 PCA plot of Raman lymphocyte spectra collected with a 21 G or
23 G needle.
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resource constraints. One of the common methods of cell pres-
ervation is cryopreservation of the lymphocyte-containing layer
in a cryoprotectant solution, such as dimethyl sulfoxide
(DMSO).
56
Freezing cells at −80 °C can have a significant impact on
the functionality and structure of biomolecules such as lipids,
proteins, and nucleic acids due to changes in hydrophobic
and hydrophilic interactions.
57
These changes may affect the
content of FTIR and Raman spectra, which provide infor-
mation on the molecular composition and structure of cells.
However, it is not yet clear how a single freeze–thaw cycle influ-
ences the biochemical information of lymphocytes obtained
from these spectroscopic techniques.
The mean FTIR and Raman spectra of non-frozen lympho-
cytes, i.e. processed immediately after the drawing of blood
and frozen lymphocytes, i.e. a single freeze–thaw cycle can be
seen in ESI Fig. 5.†
The PCA data of the FTIR spectra (Fig. 5A) cluster by donor
and by preservation method, with clustering occurring along
the PC1 axis. PC1 explains 62% of the variance between the
data sets. The spectral spread across the negative PC1 axis
(Fig. 5C) is due to the spectral regions consisting of contri-
butions from C–O ribose (984–1000 cm
−1
), glycogen, collagen,
and phosphodiester groups of nucleic acids (1034–1046 cm
−1
),
β-sheet structure of amide I and CvC of thymine, adenine
(1630–1646 cm
−1
), amide I and νCvCcis of lipids, fatty acids
(1660–1670 cm
−1
), amide I and CvO guanine deformation N–
H in plane (1680–1692 cm
−1
). Variations in amide I vibrations
that originate in the protein backbone indicate changes in the
structure of secondary proteins and suggest instability of bio-
molecules after freezing at −80 °C.
58
The PCA score plot of Raman spectra (Fig. 5B) demonstrates
discrimination occurring across the PC1 axis, which accounts
for 18% of the variance. Negative PC1 loadings (Fig. 5D)
correspond to spectral regions dominated by contributions
from O–P–O stretching of DNA (769–812 cm
−1
), phospholipids
(1077–1107 cm
−1
), tryptophan and lipids (1361–1388 cm
−1
),
DNA (1476–1495 cm
−1
), and phenylalanine, tyrosine, and cyto-
sine (1563–1602 cm
−1
). In the literature, a decrease in protein,
lipids, and DNA after freezing has previously been reported in
plasma, calf thymus DNA, domestic cat oocytes and embryos,
and prostate tumour cells.
54,57,59–61
To increase the rigour of
this research, it would be necessary to replicate these findings
using a larger data set. Furthermore, this study did not
account for several factors, such as the impact of multiple
freeze–thaw cycles, long-term storage, and the temperature
and time required for effective lymphocyte thawing, on the
spectral content of lymphocytes. Investigating these aspects of
lymphocyte storage would be of great interest for future
research, especially since it is common practice in clinical set-
tings to resample patient biological material due to the low
volume of biological material usually obtained.
3.3. Sample processing
3.3.1. The effect of inter-operator variability on spectral
reproducibility. In a clinical setting or its affiliated testing lab-
oratories, it is a common practice for multiple analysts to
process patient samples and perform subsequent instrumental
analysis after blood collection. This part of the study aimed to
evaluate the inter-operator reproducibility of lymphocyte spec-
tral profiles in a typical clinical sample processing workflow by
comparing the results obtained by two operators with identical
training but varying experience, who processed volunteer
blood samples collected on the same day by the same phlebo-
tomist. The blood samples were analysed on the same FTIR
and Raman spectrometer.
Fig. 5 PCA score plot of FTIR (A) and Raman (B) spectra of non-frozen (processed immediately) and frozen lymphocytes (single freeze–thaw cycle),
PC1 loading of FTIR lymphocyte spectra (C) and PC1 loading of Raman lymphocyte spectra (D). Covariance ellipses (95% confidence limit) in (A) and
(B) are shown for each data class.
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Within the mean FTIR spectral data sets of both operators
(Fig. 6A), large variations in spectral regions associated with
contributions from vibrational modes of C–C and C–O of deox-
yribose (956–976 cm
−1
), νC–O of carbohydrates, DNA, glyco-
gen, phosphodiester, and phospholipids (998–1144 cm
−1
) were
observed. For operator 1, large intensity variation was observed
in the range (1492–1540 cm
−1
); this spectral intensity trend
was not found in the operator 2 dataset. Within the operator 2
dataset, large intensity-related variation was also observed in
the spectral region dominated by molecular signatures from
amide II, C–N stretching of cytosine and guanine
(1256–1368 cm
−1
), α-sheet of amide II (1542–1552 cm
−1
),
amide II (1566–1600 cm
−1
), CvO of thymine and guanine
(1700–1722 cm
−1
), and CvO stretching of lipids and fatty acid
ester bond (1728–1746 cm
−1
).
PCA (Fig. 6C) showed overlapping covariance ellipses with
operator 1 forming a tight spectral datapoint cluster indicating
spectral homogeneity. However, the spectral spread within the
operator 2 data set can be seen across the positive PC1 and
negative PC2 axis, accounting for 51% and 15% of the var-
iance, respectively. Positive PC1 loadings (Fig. 6E) correspond
to the spectral regions consisting of contributions from
β-sheet structure of amide I, while negative PC2 loadings
(Fig. 6E) correspond to spectral regions associated with α-helix
of amide I. This change in protein confirmation and/or
protein concentration is most likely due to the handling tech-
nique of operator 2. The process of isolating peripheral PBMCs
from whole blood using density gradient separation is a multi-
step procedure that demands a high level of technical profi-
ciency and attention to detail from the operator. Any deviation
from protocols during the various washing and centrifugation
steps can negatively impact PBMCs, ultimately leading to
alterations in the spectral integrity of lymphocytes.
The mean Raman spectra of lymphocytes processed by
operators 1 and 2 are shown in Fig. 6B. Visually, operator 2
contains major spectral-intensity related variation in the
regions consisting of contributions from ring breathing modes
of uracil, thymine, and cytosine (775–796 cm
−1
), ring breathing
modes of thymine, guanine, and adenine (1360–1380 cm
−1
),
amide I and CvC stretch of lipids (1649–1669 cm
−1
), and tri-
glycerides, lipids, and CH
2
bending mode of proteins and
lipids (1441–1452 cm
−1
). The most prevalent spectral intensity-
related variation can be observed over the wavenumber range
of 1037–1057 cm
−1
. A PCA model (Fig. 6D) was further con-
Fig. 6 Mean FTIR (A) and Raman (B) spectra of lymphocytes processed by operator 1 and 2, PCA score plot of FTIR (C) and Raman (D) spectra, PC1
and PC2 loading of FTIR spectra (E), PC1 loading of Raman spectra (F). Covariance ellipses (95% confidence limit) in (C) are shown for each data
class.
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structed to aid in interpretation of these spectral alternations.
The PCA plot shows that the spectral data points of operator
1 hug the central axis, while operator 2 shows spread across
the positive PC1 axis, with PC1 accounting for 37% of variance.
The positive PC1 loadings (Fig. 6F) display the largest variance
in the region of 1037–1057 cm
−1
. However, it is shown in these
results that with proper training and due diligence, an analyst
can consistently generate accurate results while minimising
any impact on the spectral integrity of the lymphocytes, as evi-
denced by the results produced by operator 1.
3.3.2. Sample storage time: time to lymphocyte isolation 1
and 24 hours post-sample collection. Despite the widespread
use of whole blood in various clinical and research settings,
our understanding of the impact of short term storage time at
ambient temperature on the spectral quality of lymphocytes
remains limited. In an effort to address this knowledge gap,
this part of the study was conducted using volunteer blood
samples that were processed for lymphocyte isolation at two
time points: within 1 hour of collection and 24 hours post-col-
lection. Whole blood samples within each storage group were
stored for an identical amount of time before lymphocyte iso-
lation. The mean FTIR spectra of both datasets are presented
in Fig. 7A. Compared to lymphocytes that were processed
1 hour post-collection, lymphocytes processed 24 hours post-
collection displayed considerable variability in the spectral
regions associated with contributions from DNA, glycogen,
carbohydrates (1018–1066 cm
−1
), collagen, and phosphodie-
ster groups of nucleic acids (1067–1112 cm
−1
). Other regions
of the spectrum that demonstrated large variability in intensity
include contributions from biomolecules such as νC–Oofpro-
teins and carbohydrates, CO stretching of the C–OH groups of
serine, threonine, and tyrosine, amide III and phosphate
stretching bands (1138–1224 cm
−1
), amide III, phosphate
vibration of nucleic acids and vibrational modes of collagen
(1225–1306 cm
−1
), amide II, CvN of adenine and cytosine
(1492–1554 cm
−1
), and vibrational modes of amide I and
nucleic acids (1640–1658 cm
−1
).
The PCA score plot of FTIR spectra (Fig. 7C) displays two
distinct spectral data class clusters and shows discrimination
across the PC2 axis, which accounts for 10% of variance
between the data classes. Negative PC2 loadings (Fig. 7E) are
dominated by spectral regions associated with collagen &
phosphodiester groups of nucleic acids (1026–1052 cm
−1
),
amide I (CvO stretching) and CvO, CvN, N–H of adenine,
thymine, guanine, and cytosine (1642–1652 cm
−1
), amide I
and νCvCcis of lipids and fatty acids (1660–1668 cm
−1
), and
Fig. 7 Mean FTIR (A) and Raman (B) spectra of lymphocytes processed immediately and 24 hours after collection, PCA score plot of FTIR (C) and
Raman (D) spectra, PC2 loading of FTIR spectra (E), PC1 loading of Raman spectra (F). Covariance ellipses (95% confidence limit) in (C) are shown for
each data class.
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amide I (1688–1700 cm
−1
). While positive PC2 loadings are
dominated with spectral regions with contributions from
carbohydrates: glucose and glycogen (1010–1026 cm
−1
), C–O
stretching of deoxyribose (1052–1074 cm
−1
), C–O stretching
vibration of C–OH group of ribose (1110–1136 cm
−1
), amide II
(1530–1542 cm
−1
), and amide I (1652–1660 cm
−1
). The highest
variance exists for amide I, nucleic acids, and carbohydrates.
The processing of lymphocytes after 24 hours of a venous
blood draw results in the introduction of variance in spectral
regions dominated by contributions from numerous Raman
molecular fingerprints (Fig. 7B) such as glycogen and caroten-
oids (1133–1167 cm
−1
), CH
2
/CH
3
twisting of lipids and col-
lagen, (1301–1321 cm
−1
), CH
3
band (1387–1407 cm
−1
), cyto-
sine, adenine, amide II and tryptophan (1499–1562 cm
−1
), and
amide I and CvC stretch of lipids (1647–1671 cm
−1
).
Processed within 1 hour, spectral datapoints are spread into
three distinct clusters across the PC2 and PC3 axes, while pro-
cessed after 24 hours, spectral datapoints are spread across the
PC2 axis while also demonstrating spread across the negative
PC1 axis. The first three PCs account for 41%, 23% and 13%,
respectively (Fig. 7D). Negative PC1 loadings (Fig. 7F) consist
of spectral regions tentatively assigned to Raman signatures
representing C–N stretching of protein vibrations, glycogen,
and carotenoids (1111–1167 cm
−1
), CH
3
band
(1380–1411 cm
−1
), and ring breathing mode of DNA bases and
amide II (1498–1560 cm
−1
). Based on the Raman spectra pre-
sented here, it is possible to detect Raman signatures that
highlight protein-related structural changes and cellular stress
in lymphocytes processed after 24 hours.
Whole blood collection often occurs at a remote location
from the laboratory where sample processing takes place,
resulting in a delay between collection and laboratory proces-
sing.
62
According to the European Directorate for the Quality
of Medicines & Healthcare,
63
it is standard practice in Europe
to store blood for up to 24 hours at ambient temperature.
However, a recent study by Hope et al.
64
has shown that
storage of blood samples for 24 hours at ambient temperature
does not negatively impact PBMC viability and total viable cell
numbers when compared to samples stored at 4 °C. Despite
these previous findings, 24 hours ambient storage of whole
blood can introduce variance in FTIR spectra, particularly in
the 1002–1072 cm
−1
region, which can have a detrimental
effect on downstream spectroscopic analysis and classification.
Therefore, spectroscopists working with whole blood samples
stored at 24 hours at ambient temperature must exercise
caution and consider this factor when developing and inter-
preting machine learning models. The findings of the
24 hours processed lymphocytes analysed by RS revealed varia-
bility in their biochemical response, despite being processed
under identical experimental conditions. This suggests that
lymphocytes processed in this manner may exhibit varying
levels of cell injury, which could be attributed to suboptimal
storage conditions. These findings underscore the need for
further investigation using a larger cohort in order to fully
assess the impact of processing lymphocytes after 24 hours on
intra-sample spectral variation.
It is acknowledged that lymphocyte isolation from PBMCs
by density gradient separation does not ensure a pure lympho-
cyte population will be obtained following cell culture. This
issue can be minimised by selecting cells on visual character-
istics with the coupled microscope before recording with the
Raman and FTIR instrumentation.
4. Conclusion
To successfully implement FTIR and RS in clinical settings, it
is essential to identify sources of pre-analytical variability,
establish standardised protocols, and ensure the accuracy,
quality, and reproducibility of spectral data. Our study investi-
gated pre-analytical variables related to sample preparation,
storage, and processing, and the results confirmed the
following:
•The S-Monovette® blood collection system and anti-
coagulants had minimal impact on FTIR and Raman spectral
outcomes.
•Vacuette®-collected lymphocytes had little impact on lym-
phocyte FTIR spectral quality, but Raman spectra of lympho-
cytes possessed signals of cellular injury.
•Common needles (21 G or 23 G) had little impact on the
spectral profile of lymphocytes.
•Although DMSO was used as a cryoprotectant to prevent
cellular membrane damage, FTIR and RS can detect biochemi-
cal differences present in cells after a single freeze–thaw cycle
at −80 °C.
•Adequate training is a critical element for spectral repro-
ducibility, and although the issue is not fully resolved in this
current study, a foundation is provided to improve reproduci-
bility in spectral measurements.
•Processing blood samples for lymphocyte isolation
24 hours after collection had a significant negative impact on
the integrity of lymphocyte spectra, particularly in the case of
FTIR analysis.
Developing a standardised protocol for lymphocyte spectro-
scopic analysis will ensure fitness of use of lymphocyte
samples, eliminate the influence of external factors, reduce
costs associated with addressing pre-analytical errors, and
enhance the credibility of results in future studies. This study
is limited by its small sample size, and further research is
needed to establish fully standardised protocols.
Data availability
The data supporting the findings of this study are available
from the corresponding author upon reasonable request.
Author contributions
A. D. M., F. M. L., J. F. M. and D. C. conceived the study, with
A. D. M. and F. M. L. supervising the study. C. W. assisted
D. C. with the collection of volunteer blood. J. F. M. and
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D. C. performed FTIR and Raman analyses, respectively.
J. F. M. performed multivariate analysis, manuscript writing,
and design of the illustrations. A. D. M. developed in-house
software for the purpose of spectral calibration and analysis.
All authors contributed equally to reviewing and editing the
manuscript.
Conflicts of interest
The authors declare no conflict of interest.
Acknowledgements
J. F. M. is funded by Fiosraigh Enterprise Award co-funded by
TU Dublin and St Luke’s Institute for Cancer Research. D. C.
was funded by a TU Dublin Fiosraigh scholarship.
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