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\begin{document}

\title{A systematic evaluation of quenching and extraction procedures for
quantitative metabolome profiling of Hela carcinoma cell under 2D and 3D
cell culture conditions}



\author[1]{Tong Wang}%
\author[1]{Xueting Wang}%
\author[1]{Yingping Zhuang}%
\author[1]{Guan Wang}%
\affil[1]{East China University of Science and Technology}%


\vspace{-1em}



  
  \date{June 18, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
Metabolic reprogramming has been coined as a hallmark of cancer,
accompanied by which the alterations in metabolite levels have profound
effects on gene expression, cellular differentiation and the tumor
environment. Yet a systematic evaluation of quenching and extraction
procedures for quantitative metabolome profiling of tumor cells is
currently lacking. To achieve this, this study is aimed at establishing
an unbiased and leakage-free metabolome preparation protocol for Hela
carcinoma cell. We evaluated 12 combinations of quenching and extraction
methods from three quenchers (liquid nitrogen, -40\selectlanguage{ngerman}°C 50\% methanol,
0.5°C normal saline) and four extractants (80\% methanol, methanol:
chloroform: water (1:1:1, v/v/v), 50\% acetonitrile, 75°C 70\% ethanol)
for global metabolite profiling of adherent Hela carcinoma cells. Based
on the isotope dilution mass spectrometry (IDMS) method, gas/liquid
chromatography in tandem with mass spectrometry was used to
quantitatively determine 43 metabolites including sugar phosphates,
organic acids, amino acids, adenosine nucleotides and coenzymes involved
in central carbon metabolism. Among 12 combinations, cells that washed
twice with phosphate buffered saline (PBS), quenched with liquid
nitrogen, and then extracted with 50\% acetonitrile was found to be the
most optimal method to acquire intracellular metabolites with minimal
loss during sample preparation. Furthermore, a case study was carried
out to evaluate the effect of doxorubicin (DOX) on both adherent cells
and 3D tumor spheroids using quantitative metabolite profiling. Based on
this, quantitative time-resolved metabolite data can serve to the
generation of hypotheses on metabolic reprogramming to reveal its
important role in tumor development and treatment.%
\end{abstract}\selectlanguage{ngerman}%



\sloppy


\textbf{A systematic evaluation of quenching and extraction procedures
for quantitative metabolome profiling of Hela carcinoma cell under2D and
3D cell culture conditions}

Tong Wang\textsuperscript{1}, Xueting Wang\textsuperscript{1}, Yingping
Zhuang\textsuperscript{1,2}, Guan Wang\textsuperscript{1,2}*

1. State Key Laboratory of Bioreactor Engineering, East China University
of Science and Technology (ECUST), Shanghai, People's Republic of China.

2. Qingdao Innovation Institute of East China University of Science and
Technology, Shanghai,

People's Republic of China.

*Correspondence: guanwang@ecust.edu.cn

\textbf{Abstract}

Metabolic reprogramming has been coined as a hallmark of cancer,
accompanied by which the alterations in metabolite levels have profound
effects on gene expression, cellular differentiation and the tumor
environment. Yet a systematic evaluation of quenching and extraction
procedures for quantitative metabolome profiling of tumor cells is
currently lacking. To achieve this, this study is aimed at establishing
an unbiased and leakage-free metabolome preparation protocol for Hela
carcinoma cell. We evaluated 12 combinations of quenching and extraction
methods from three quenchers (liquid nitrogen, -40\selectlanguage{ngerman}°C 50\% methanol,
0.5°C normal saline) and four extractants (80\% methanol, methanol:
chloroform: water (1:1:1, v/v/v), 50\% acetonitrile, 75°C 70\% ethanol)
for global metabolite profiling of adherent Hela carcinoma cells. Based
on the isotope dilution mass spectrometry (IDMS) method, gas/liquid
chromatography in tandem with mass spectrometry was used to
quantitatively determine 43 metabolites including sugar phosphates,
organic acids, amino acids, adenosine nucleotides and coenzymes involved
in central carbon metabolism. According to the number, amount, and
reproducibility of extracted metabolites, we found that 50\%
acetonitrile was an excellent extractant for Hela cells while 1:1:1
methanol: chloroform: water behaved the worst extraction efficiency. The
results showed that the total amount of the intracellular metabolites in
cell extracts obtained using different sample preparation procedures
with the IDMS method ranged from 21.51 to 295.33 nmol/million cells.
Consistent with previous study, metabolite leakage was found to be
maximal with cold methanol as the quencher. Among 12 combinations, cells
that washed twice with phosphate buffered saline (PBS), quenched with
liquid nitrogen, and then extracted with 50\% acetonitrile was found to
be the most optimal method to acquire intracellular metabolites with
minimal loss during sample preparation. In addition, the same conclusion
was drawn as these 12 combinations were applied to obtain quantitative
metabolome data from three-dimensional (3D) tumor spheroids.
Furthermore, a case study was carried out to evaluate the effect of
doxorubicin (DOX) on both adherent cells and 3D tumor spheroids using
quantitative metabolite profiling. Extracellular metabolite
concentrations showed that 3D cells performed an enhanced glucose
consumption and lactate excretion rate than two-dimensional (2D) cells
before and after the addition of DOX. While, the addition of DOX did not
interfere with extracellular glutamine concentration in both 2D and 3D
cells. Pathway enrichment analysis using targeted metabolomics data
showed that DOX exposure would significantly affect amino acid
metabolism-related pathways, which might be related to the mitigation of
redox stress. Strikingly, our data suggested that compared to 2D cells
the increased intracellular glutamine level in 3D cells benefited
replenishing the tricarboxylic acid (TCA) cycle when the glycolysis was
limited after dosing with DOX. Taken together, this study provides a
well-established quenching and extraction protocol for quantitative
metabolome profiling of Hela carcinoma cell under 2D and 3D cell culture
conditions. Based on this, quantitative time-resolved metabolite data
can serve to the generation of hypotheses on metabolic reprogramming to
reveal its important role in tumor development and treatment.

\textbf{Keywords} : Hela; metabolomics; sample preparation; 3D tumor
spheroids; quenching; extraction; isotope dilution mass spectrometry

\textbf{Graphical abstract}

Optimized harvesting, quenching and extraction protocols for both 2D and
3D Hela carcinoma cell metabolomics analysis.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{1. Introduction}

Cancer is the top two cause of death among people under the age of 70 in
most countries, which has long been a serious threat to human life and
health {[}1{]}. Cancer is caused by spontaneous or environmental factors
induced genetic mutations which involves abnormal cell growth with the
potential to invade or spread to other parts of the body {[}2{]}. As the
direct and indirect consequences of carcinogenic mutations,
tumorigenesis largely depends on the reprogramming of cell metabolism
with metabolic changes into six hallmarks such as dysregulated nutrient
uptake kinetics, enhanced opportunistic modes of nutrient acquisition,
increased used of central intermediates for biosynthesis and
nicotinamide adenine dinucleotide phosphate (NADPH) production,
increased demand for nitrogen, altered metabolite-driven gene
regulation, and metabolic interactions with the microenvironment
{[}3{]}. For example, Cairns et al found that the glucose uptake rate of
tumor cells could reach about 10 times that of normal cells {[}4{]}, and
Patra et al. found that the expression of hexokinase 2 (HK2) was
significantly up-regulated and HK2 ablation inhibited the neoplastic
phenotype of human lung and breast cancer cells in vitro and in vivo
{[}5{]}. Spinelli et al. found that breast cancer cells can recycle
waste by-product ''ammonia'' as nitrogen source to promote tumor growth
{[}6{]}. As a result, these specific hallmarks exhibited by an
individual tumor can confer a druggable metabolic vulnerability
sufficient to open a therapeutic window. For example, the expression of
PUMILIO 1/2 (PUM1/2) was increased in colorectal cancer (CRC). Gong et
al. found that knockdown of Pum1 and/or Pum2 in human CRC cells resulted
in a significant reduction in tumorigenicity and intravenous injection
of nanoparticle-encapsulated anti-Pum1 and Pum2 siRNA reduced the growth
of CRC cells. These findings revealed the potential of PUM proteins as
therapeutic targets for CRC {[}7{]}. Non-small-cell lung cancer (NSCLC)
patients with activating epidermal growth factor receptor (EGFR)
mutations developed resistance to tyrosine kinase inhibitor (TKI). Wang
et al. found that EGFR C797S mutation was closely related to AXL through
transcriptome and proteome analysis, and inhibition of AXL could
effectively slow down the growth of NSCLC cells harboring EGFR C797S.
This finding suggested that AXL inhibition might be a potential adjuvant
therapy for NSCLC harboring the EGFR C797S mutation {[}8{]}.

Metabolome defines a set of metabolites in a biological sample, which
provides snapshots of tumor cells in response to genetic and/or
therapeutic perturbations. Metabolic footprinting has been used to
identify tumor biomarkers for cancer diagnosis and prognosis, and such
biomarkers can be assayed in non-invasively collected biofluids like
blood or serum {[}9-11{]}. Recently, the combination of cancer models
with metabolomic analysis has been shown to be a powerful approach to
generate new hypotheses about predicting drug susceptibility, resistance
and mode of action {[}12{]}. For example, JAIN et al. found that
interfering with glycine metabolism slowed the growth of rapidly
proliferating cancer cells and suggested that the increased dependence
of rapidly growing cancer cells on glycine might provide a potential
target for therapeutic intervention {[}13{]}. Newman et al. found that
cancer cells could utilize serine as a major source of carbon units and
it allowed cancer cells to maintain high proliferation rates {[}14{]}.
Tao et al. observed 270 dysregulated lipids in serum exosomes between
pancreatic cancer (PC) patients and healthy controls through
non-targeted lipidomic analysis, which might become diagnostic
biomarkers {[}15{]}. Through metabolomic analysis, Bermudez et al.
assessed proliferation of a collection of cancer cells following
inhibition of the mitochondrial electron transport chain (ETC) and found
that cell lines least sensitive to ETC inhibition maintained
intracellular aspartate levels by importing it. This suggested that
aspartate was the limiting metabolite for tumor growth and may be a
target for cancer therapy {[}16{]}.

In order to exploit pivotal intracellular mechanisms associated with
tumor growth and drug resistance requires an insightful investigation of
intracellular metabolites. To achieve this, sample preparation and
analytical protocols should be developed and evaluated in terms of
reproducibility and number of metabolite features obtained {[}17{]}.
Nevertheless, acquiring metabolome changes is experimentally demanding,
such that dynamic data in the development, progression and therapeutic
treatment of cancer are extremely scarce. Meanwhile, achieving the
absolute quantification of global metabolite is further hampered by
sample pretreatment with loss and biased mass spectrometry-based
analyses. To obtain true snapshots of intracellular metabolites,
leakage-free sample pretreatment and unbiased metabolite determination
are required. For cell acquisition, suspension cells could be directly
centrifuged to obtain cell pellets while the adherent cells must be
detached before metabolite extraction. The adherent growth cells can be
harvested by scraping or using trypsin. Traditionally, trypsin was often
used to harvest cells. However, previous studies have concluded that
harvesting cells by trypsinization lead to the loss of a large amount of
metabolites {[}18, 19{]}. Therefore, direct quenching of cells with
physical scraping was used as an alternative. However, this was not
conducive to cell counting. To address this, measuring the protein
content of cell pellets to correct the number of cells has been reported
as a promising method for standardizing cell culture systems {[}20{]}.
Metabolite quenching is the first critical step in the sample
preparation process. The quenching process should meet several
requirements: the enzyme activity must be quenched as quickly as
possible to avoid metabolite degradation and changes in sample
composition. Some metabolites are extremely unstable with a short
turnaround time, such as adenosine triphosphate (ATP) and glucose
6-phosphate (G6P) {[}21{]}, so rapid quenching procedure is essential to
ensure the true state of intracellular metabolites {[}22{]}. Quenching
can usually be achieved by freezing samples in liquid nitrogen {[}23{]},
or in cold methanol solution {[}24{]}. It has been reported that cold
methanol quenching caused serious metabolite leakage in cell, resulting
in an overall loss of metabolite recovery {[}25{]}. However, in cancer
cells, a systematic evaluation of the extent of metabolite leakage among
different quenchers and the influential factors giving rise to it has
rarely been reported.

Besides leakage-free quenching method, a complete extraction of
intracellular metabolite is required. In previous studies, a lot of
efforts have been devoted to the development of extraction methods for
intracellular metabolites in different mammalian cells. For instance,
Sellick et al. used 100\% methanol extraction twice and then water once
to extract the metabolites, which could recover the largest range of
intracellular metabolites from Chinese hamster ovary (CHO) cells
{[}26{]}. However, at the same time, other extraction method should be
required to maximize the non-polar metabolites recovered. Dettmer et al.
used methanol/water to scrap cells harvest and extract metabolites from
adherently growing SW480 cells {[}18{]}. Wamelink et al. used 50\%
acetonitrile to extract intermediates in pentose phosphate pathway in
fibroblasts and lymphoblasts {[}27{]}. However, these extraction methods
have not yet been evaluated together with quenching procedures to
develop a generalized protocol for acquiring intracellular metabolome in
mammalian cells. Furthermore, obtaining accurate concentrations of
intracellular metabolites were of great significance for the validation
of metabolic fluxes and the establishment of metabolic models {[}22, 28,
29{]}. Mass spectrometry such as Gas Chromatography-Mass Spectrometry
(GC-MS) and Liquid Chromatography tandem Mass Spectrometry (LC-MS/MS)
has been widely used in the determination of metabolites due to its high
accuracy and high resolution {[}30, 31{]}. However, sample recovery
needs to be determined experimentally for each analyte. To circumvent
this, the stable isotope internal standard of the analyte was the best
choice, because the substance could be distinguished by mass
spectrometry, and IDMS could be used to track and correct the loss of
the sample during pretreatment and analysis {[}32{]}. The stable isotope
internal standards could be cultivated on a medium with uniformly
labeled \textsuperscript{13}C-glucose as the sole carbon source, and
the\textsuperscript{13}C-labeled internal standard of intermediate
metabolites and target metabolites could be obtained through the
self-metabolism of microbial cells {[}33{]}. By combining GC-MS/LC-MS/MS
with the IDMS method, it is possible to track losses during sample
pretreatment and analytical process {[}32, 33{]} and greatly increase
the accuracy of metabolite quantitation. Furthermore, previously
developed sample preparation methods were merely developed for 2D
adherent cells, which cannot be directly applied to 3D tumor model
without further evaluation. As is known, the 2D cells cannot simulate
the complex structure and heterogeneity of tumor tissue in vivo {[}34,
35{]}. 3D multicellular tumor spheroids (3D MTSs) are a tumor model
between 2D monolayer cell culture and animal models {[}36{]}. Compared
with traditional 2D model, the internal concentration gradient of
nutrients and oxygen led to outer proliferation of living cells, inner
accumulation of necrotic cells and living quiescent cells in the
transition zone {[}37{]}. This inhomogeneity is an important feature of
solid tumors in vivo. Many anticancer drugs exert cytotoxic effects on
proliferating cells, while quiescent cells escape treatment {[}38,
39{]}. Therefore, model systems such as 3D MTS involving quiescent cells
are critical for preclinical drug screening and drug resistance
investigations.

In this study, we systematically evaluated 12 sample preparation
protocols for accurate determination of intracellular metabolites in
both 2D and 3D tumor cell models. Further, based on the optimal sample
preparation protocol, quantitative metabolomics analysis was used to
discover the differential metabolome features of both 2D and 3D tumor
models in response to DOX treatment.

\textbf{2. Materials and methods}

\textbf{2.1 Cell lines and media}

HeLa carcinoma cells were purchased from American Type Culture
Collection (ATCC, Manassas, VA, USA). Cells were cultured in dulbecco's
modified eagle medium (DMEM) (Gibco, 11995-065, USA) supplemented with
10\% fetal bovine serum (Gibco, 10099-141, USA), 100 IU/mL penicillin
and 100 mg/mL streptomycin (B540732, Sangon Biotech, Shanghai, CN).
Cells were maintained in 75 cm\textsuperscript{2} flasks in a 37\selectlanguage{ngerman}°C, 5\%
carbon dioxide incubator (Thermo Fisher Scientific, Waltham, MA, USA)
and passaged when the confluence of cells reached 80\%--90\%.

\textbf{2.2 Rapid generation of single tumor spheroid}

To aggregate cells into a spheroid, cells in the exponential growth
phase were digested with 2.5\% trypsin, diluted to 10,000 cells/mL,
quickly mixed with 2.5\% volume fraction of Matrigel (356,237, Corning,
NY, USA), and seeded into a ''U'' type ultra-low attachment 96-well
plate (Corning 7007, USA) at 200 \selectlanguage{greek}μ\selectlanguage{english}L/well. The 96-well plates were then
centrifuged at 1000\selectlanguage{ngerman}×g for 10 min, and incubated in a 37°C, 5\%
CO\textsubscript{2} incubator.

\textbf{2.3 The addition of DOX}

2D monolayer culture: 2D cells were cultured at a seeding density of
3.5×10\textsuperscript{5} cells/mL for 48 h. 3D MTS: HeLa cells were
cultured at a density of 2000 cells/well for 6 days. Then removed the
medium and incubated with 1\selectlanguage{greek}μ\selectlanguage{english}M DOX (dissolved in medium) for 48 h.

\textbf{2.4 Preparation of}
\textbf{uniformly\textsuperscript{13}C-labeled cell extract}

\emph{Pichia pastoris} G/DSEL strains were cultivated
with\textsuperscript{13}C fully-labeled glucose as the sole carbon
source, and to increase the intracellular abundance of metabolites in
the central carbon metabolism, the \textsuperscript{13}C fully-labeled
glucose pulses were applied. The detailed operating procedures was
performed as described earlier {[}40{]}. The prepared cell extracts were
split and stored at -80\selectlanguage{ngerman}°C pending for use. The extracts (100 \selectlanguage{greek}μ\selectlanguage{english}L)
mentioned above were added before intracellular metabolite extraction.
Based on the IDMS theory, this internal standard could track the loss of
metabolites during sample pretreatment and MS-based analytical process
{[}41{]}.

\textbf{2.5 Quenching and extraction procedures for intracellular
metabolites}

2D Hela cells were harvested after 48 h of incubation. The growth medium
was removed and the flasks were washed by 5 mL PBS twice to remove
extracellular metabolites. In all experiments, 2D cells were quenched on
the flask surface and then scraped with extraction solvent. Cells were
quenched and metabolites were extracted using 12 combinations of
quenching and extraction methods as described below (\textbf{Figure 1}
).

MTSs were harvested at 6th day and collected in 10 mL centrifuge tubes.
The cell suspension was centrifuged at 1000\selectlanguage{ngerman}×g for 5 min, the media was
removed and the cell pellet was washed twice with 5mL PBS to remove
extracellular metabolites. Then the MTSs were digested with Accutase\selectlanguage{english}
cell digestive solution (40506ES60, Yesen, Shanghai, CN) at 37 \selectlanguage{english} for 30
min to form single-cell suspension and washed once with PBS. The single
cell suspension was centrifuged for 10 min at 1000xg and the supernatant
was removed.

\emph{1. Liquid nitrogen quenching - 80\% methanol extraction}

After being washed by PBS, 15 mL liquid nitrogen was added to quench the
2D cells for 1 min. Then the sample was added with 3 mL of 80\%
methanol, and the cells were scraped gently and fully with a cell
scraper. The cell suspension was harvested into a 10 mL centrifuge tube
and 100 \selectlanguage{greek}μ\selectlanguage{english}L yeast cell extracts as internal standards were added. Then
the cell suspension was vortexed for 30 s and quickly frozen in liquid
nitrogen. After thawing on ice at 4\selectlanguage{ngerman}°C, samples were centrifuged at
1000×g for 10 min and the supernatant was collected. The freeze-thaw
process was repeated twice. Finally, the cell pellet was extracted by
water. For the water extraction, the cell pellet was resuspended in 1 mL
ice-cold milliQ water followed by flash freezing in liquid nitrogen.
After thawing on ice at 4°C, samples were centrifuged at 12000×g for 10
min and the supernatant was collected. The methanol and water extracts
were pooled together, centrifuged at 12000×g for 10 min and the
supernatant was collected and stored at -80 °C.

The cell pellets of MTSs were prepared as mentioned above and collected
in 10 mL centrifuge tubes, which were then frozen in liquid nitrogen for
1 min. The subsequent quenching and extraction methods were performed
the same as 2D cells.

\emph{2. Liquid nitrogen quenching - 50\% acetonitrile extraction}

The quenching and extraction were performed as described for the Liquid
nitrogen quenching-methanol extraction where 80\% methanol was replaced
by 50\% acetonitrile.

\emph{3. Liquid nitrogen quenching - 1:1:1 methanol: chloroform: water
extraction}

The quenching and extraction were also performed as described for the
Liquid nitrogen quenching-methanol extraction where 80\% methanol was
replaced with 1:1:1 methanol: chloroform: water.

\emph{4. Liquid nitrogen quenching - 70\% ethanol extraction}

The quenching step was performed as described above. Then the sample was
added with 3 mL of 70\% ethanol (75\selectlanguage{english}), and the cells were scraped gently
and fully with a cell scraper. The cell suspension was harvested into a
10 mL centrifuge tube and 100 \selectlanguage{greek}μ\selectlanguage{english}L internal standards were added. The cell
suspension was quickly put in 75\selectlanguage{ngerman}°C water for 3 min, then centrifuged at
1000×g for 10 min and the supernatant was removed. The cell pellet was
resuspended in a 3 mL of the same ethanol solution and the extraction
repeated. Finally, the cell pellet was resuspended in 1 mL ice-cold
milliQ water followed by 75°C water bath for 3 min. The ethanol and
water extracts were pooled together, centrifuged at 12000×g for 10 min
and the supernatant was collected and stored at -80°C.

\emph{5. 50\% Methanol quenching - 80\% methanol extraction}

The cells were rapidly quenched by 10 mL 50\% methanol (-40°C). Then the
quenching solution was removed and stored at -80\selectlanguage{english}. The metabolites were
extracted from the cells using the methods as described in 2.5.1.

\emph{6. 50\% Methanol quenching - 50\% acetonitrile extraction}

The quenching and extraction were performed as described for the
methanol quenching-methanol extraction method as described in 2.5.5
where 80\% methanol was replaced with 50\% acetonitrile.

\emph{7. 50\% Methanol quenching - 1:1:1 methanol: chloroform: water
extraction}

The quenching and extraction were performed as described for the
methanol quenching-methanol extraction where 80\% methanol was replaced
by 1:1:1 methanol: chloroform: water.

\emph{8. 50\% Methanol quenching - 70\% ethanol extraction}

The quenching step was performed as described for the methanol
quenching. The extraction step was performed as described in 2.5.4.

\emph{9. Normal saline quenching - 80\% methanol extraction}

The cells were rapidly quenched by 10 mL normal saline (0.5degC). Then the
supernatant of quenching solution was collected and stored at -80\selectlanguage{english}. The
metabolites were extracted from the cells using the methods as described
in 2.5.1.

\emph{10. Normal saline quenching - 50\% acetonitrile extraction}

The quenching and extraction were performed as described for the normal
saline quenching-methanol extraction where 80\% methanol was replaced by
50\% acetonitrile.

\emph{11. Normal saline quenching - 1:1:1 methanol: chloroform: water
extraction}

The quenching and extraction were performed as described for the normal
saline quenching-methanol extraction where 80\% methanol was replaced by
1:1:1 methanol: chloroform: water.

\emph{12. Normal saline quenching - 70\% ethanol extraction}

The quenching step was performed as described for the normal saline
quenching. The extraction step was performed as described in 2.5.4.

\textbf{2.6 Total protein versus cell mass as a normalization strategy}

In this study, metabolites were quantified based on the weight of total
protein. Single cell suspensions from both 2D monolayer culture and 3D
MTS were centrifuged for 10 min at 12,000xg at 4\selectlanguage{english}. For measurement of
total protein amount, the pellets were resuspended in Radio
Immunoprecipitation Assay (RIPA) Lysis Buffer (Thermo Scientific\selectlanguage{english},
89901, USA). The protein concentration was determined using a
bicinchoninic acid (BCA) assay (Thermo Fischer Scientific, Waltham, MA,
USA) following the instructions of manufacturer. 5 \selectlanguage{greek}μ\selectlanguage{english}L each protein
lysate was added to 2 \selectlanguage{greek}μ\selectlanguage{english}L reagent A, followed by the addition of 100 \selectlanguage{greek}μ\selectlanguage{english}L
reagent B. After 30 min at 37, absorbance was read at 562 nm by
fluorescence microplate reader (Thermo Fisher Scientific, Waltham, MA,
USA). Protein concentrations in the sample were determined using
standard curves generated by linear regression analysis.

\textbf{2.7 Metabolite sample pretreatment and MS-based analytical
procedures}

The above-mentioned sample extracts were evaporated to dryness using
Rap-Vap (Labconco, Kansas City, MO). The obtained residue was
redissolved in deionized water to obtain a final weight of 600 \selectlanguage{greek}µ\selectlanguage{english}g.
Subsequently, the extracts were split and stored at -80 until analysis.

Samples for amino acids quantification were analyzed using GC-MS (7890A
GC, Agilent, Santa Clara, CA, USA) instrument coupled to a 5975C MSD
single quadrupole mass spectrometer (Agilent, Santa Clara, CA, USA).
Briefly, 100 \selectlanguage{greek}µ\selectlanguage{english}L cell extracts were freeze-dried and derivatized by
adding 75 \selectlanguage{greek}μ\selectlanguage{english}L acetonitrile and 75 \selectlanguage{greek}μ\selectlanguage{english}L N-methyl-N-
(tert-butyldimethylsilyl) trifluoroacetamide (MTBSTFA) at 70\selectlanguage{ngerman}°C for 60
min. Then the derivatized sample was cooled to room temperature and
centrifuged at 12000×g for 2 min. The supernatant was collected for
further analysis.

Samples for organic acids, sugar phosphates, adenosine nucleotides and
coenzymes were analyzed using LC-MS/MS (Dionex Ultimate 3000 UPLC system
coupled to TSQ Quantum Ultra mass spectrometer, Thermo Scientific). The
absolute quantification of these metabolites was achieved using GC-IDMS
and LC-MS/MS-IDMS methods {[}42{]}. The details of the analytical
procedure have been described elsewhere {[}22{]}.

\textbf{2.8 Data statistical analysis}

All data in this study were expressed as mean ± standard deviation (SD).
Data analysis was performed by GraphPad Prism 8.0. The heatmap was
generated using the pheatmap package (R version 4.0.1). The pathway
enrichment was performed using the MetaboAnalyst
(https://www.metaboanalyst.ca/). Significant analysis was performed by
GraphPad Prism 8.0, with p \textless{} 0.05, p \textless{} 0.01 and p
\textless{} 0.001 indicated that there were significant (*), highly
significant (**) and extremely significant (***) difference between the
groups, respectively.

\textbf{3. Results and discussion}

\textbf{3. 1 Define the optimal quenching and extraction procedure for
quantitative metabolomics}

Accurate determination of intracellular metabolite levels requires
well-established procedures for sampling and sample pretreatment. The
ideal procedure should immediately quench cellular metabolism and
quantitatively extract all metabolites, while a significant challenge
associated with the rapid turnover, varied abundance and physicochemical
diversity of intracellular metabolites still requires systematic and
quantitative evaluation. Otherwise, the obtained biased metabolite level
will lead to misinterpretation of functional information about the
biochemical and physiological states of cells {[}40{]}. In recent years,
different quenching and extraction methods have been proposed and used
for quantitative metabolomics of mammalian cells including tumor cells
(\textbf{Table 1} ). Early quenching studies on microorganisms
established the use of cold methanol as a standard quenching protocol
for its high efficiency of freezing enzyme activities {[}43-45{]}, and
cold methanol has also been reported frequently as the quenching agent
in mammalian cell metabolomics {[}20, 24, 46, 47{]}. In addition to cold
methanol, the use of cold normal saline has also become common and cold
0.9\% (w/v) NaCl has been preferably used for quenching mammalian cell
metabolism with minimal metabolite leakage {[}48{]}. Meanwhile, liquid
nitrogen was also a commonly used quenching method in recent years which
could rapidly and efficiently stop cell metabolism {[}18, 49-52{]}.
After cell harvesting and quenching, metabolite extraction was the next
critical step. Several methods existed for metabolite extraction, but
the literatures were sometimes contradictory regarding the adequacy and
performance of each technique {[}41{]}. To the best of our knowledge,
the polarity of metabolites ranged from very hydrophilic to hydrophobic
compounds, and polar metabolites were supposed to be extracted with
ethanol, methanol, acetonitrile, or other mixtures of these solvents,
etc {[}18, 24, 26, 46, 48, 51, 52{]}, while more lipophilic metabolites
could be extracted by chloroform/methanol/water mixture, etc {[}20, 47,
49{]}. At the same time, in order to ensure that the extraction of
intracellular metabolites was as complete as possible, the extraction
was usually combined with several freeze-thaw cycles {[}18{]}.

However, no previous study has yet simultaneously optimized these
critical procedures, i.e., quenching and extraction, to explore cell
metabolomics using a strictly quantitative approach. Therefore, in this
study, we systematically evaluated 12 combinations of three commonly
used quenchers and four extractants using an IDMS approach and the
specific experimental workflow were shown in \textbf{Figure 1} . The
prepared samples were analyzed using gas/liquid chromatography-IDMS
method, and a total of 43 metabolites were quantitatively determined in
Hela cells, including amino acids, sugar phosphates, organic acids,
adenosine nucleotides and coenzymes (\textbf{Figure 2} ).

As shown in \textbf{Figure 1} , the medium was first removed and the
cells attached to the flask surface were washed twice with PBS. The
conventional method generally used trypsin to detach cells from their
growth surface, which has been proven to inevitably change the profile
of cellular metabolites since the detachment of cells from the
extracellular matrix altered their physiology {[}18, 53-55{]}. In order
to avoid changing the cellular environment before quenching metabolic
activities within the cell, in this study, tumor cells cultivated in 2D
mode were directly quenched and then scraped with the extraction solvent
in all experiments. Meanwhile, 3D MTSs were first digested into single
cell suspension, and the cell pellet was obtained after centrifugation.
The subsequent operations were the same as those of the 2D cells. We
estimated the cell number by determining the protein content of cell
pellet. The standard curve of the protein was shown in \textbf{Figure
S1} . With respect to quenching efficiency, ideal quenching solvent
should immediately inhibit cellular metabolic activity and inactivate
enzymes within the cells without leaking metabolites from the cells.
Quenching of metabolism was usually achieved by rapid changes in
temperature or pH. The rate of quenching was critical because many
metabolic reactions in glycolysis, as well as those related to ATP,
featured high turnover rates in the timescale of seconds {[}45{]}.
Numerous studies have reported the adverse effects of prolonged exposure
to quenching solutions which demonstrated that the longer the cells were
exposed to the quenching solution, the exacerbated leakage of
intracellular metabolites {[}22, 45, 56{]}. This seemed reasonable that
prolonged exposure to organic solvents and cold shock could cause cell
membrane leakage, which also implied that sample processing should
proceed as quickly as possible to the extraction step. In addition,
quantitative methods should be used to determine the extent of cellular
leakage so that true intracellular metabolite concentrations could be
determined {[}45{]}. To achieve this, we quantitatively assessed the
number and amount of leaked metabolites in the quenching agent, and
investigated the factors that influenced the extent of leaked
metabolites.

In order to determine the most effective extraction method, four
different extraction methods were evaluated after Hela cells were
quenched with liquid nitrogen, 50\% methanol, and normal saline
respectively. The evaluated extraction methods included 80\% methanol,
50\% acetonitrile, methanol: chloroform: water (1:1:1, v/v/v) and 70\%
ethanol (75\selectlanguage{english}). Sellick et al. found that the addition of a water-based
extraction step combined the properties of solvent-based and water-based
methods, and extracts produced in this method showed the greatest amount
of recovered metabolites in CHO cell {[}26{]}. Therefore, we also
adopted an additional water extraction to increase metabolite coverage
for all 12 combination methods. The metabolites in the obtained cell
extracts were absolutely quantified using GC-IDMS and LC-MS/MS-IDMS
method. Subsequently, effectiveness of the quenching and extraction
procedures should be quantitatively evaluated for their suitability for
Hela carcinoma cells grown as 2D monolayer cultures and 3D MTS. For this
purpose, previous studies have reported some evaluation criteria with
respect to relative metabolite abundance and metabolite coverage. For
example, LORENZ et al. established a method for relative quantification
of metabolites in adherent mammalian cells using the clonal \selectlanguage{greek}β-\selectlanguage{english}cell line
INS-1 as a model sample, without considering absolute quantification of
metabolites {[}49{]}. Dietmair et al. evaluated several quenching and
extraction procedures for mammalian cells grown in suspension, but did
not take into account the reproducibility of each method, with large
errors in the metabolite level {[}48{]}.

To allow an accurate view of intracellular metabolome, the quenching
step should stop metabolism as quickly as possible while the extraction
should cover a wide spectrum of metabolite. Furthermore, target
metabolites should not undergo any physical or chemical modification and
degradation should be minimized during quenching and extraction process
{[}45{]}. In this study, we evaluated the compatibility of three
different quenchers such as liquid nitrogen, -40\selectlanguage{ngerman}°C 50\% methanol, 0.5°C
normal saline and four extractants (80\% methanol, methanol: chloroform:
water (1:1:1, v/v/v), 50\% acetonitrile, 75°C 70\% ethanol) for global
metabolite profiling of adherent Hela carcinoma cells. To
comprehensively evaluate 12 combinations of quenching and extraction
procedures in this study, we analyzed the acquired metabolite data based
on the following four criteria: 1) Metabolite leakage in the quenching
solution; 2) Metabolite coverage and abundance identified in cell
extracts; 3) Absolute quantitation of intracellular metabolite
concentrations; 4) Reproducibility of metabolite quantification
{[}57{]}.

\emph{3.1.1 Metabolite leakage in the quenching solution}

To study the effect of quenching solutions on the integrity of cell
membrane, the potential metabolite leakage during quenching was
evaluated by quantifying the metabolites in the quenching solution. The
metabolites analyzed include compounds with different properties, such
as amino acids, organic acids, sugar phosphates, adenosine nucleotides
and coenzymes. \textbf{Figure 3} showed the effect of these two
quenchers (50\% methanol and normal saline) on the recovery of different
representative metabolites. Liquid nitrogen did not cause intracellular
metabolites leak into the quencher because liquid nitrogen would
volatilize directly. In both cases, all determined amino acids were
found in the quenching solution. Compared with normal saline, cold
methanol gives rise to more serious leakage of intracellular amino acids
(\textbf{Figure 3a} ). Also, an obvious phenomenon showed that the more
leakage comes with the higher amounts of intracellular amino acids, such
as alanine (Ala), valine (Val), proline (Pro), lysine (Lys), aspartate
(Asp), glutamate (Glu). In consistency with amino acids, organic acids,
sugar phosphates and energy metabolism intermediates also exhibited a
significant higher leakage when tumor cells were quenched with the cold
methanol. Meanwhile, the results also indicated that intracellular sugar
phosphates were more inclined to leak in response to cold methanol
treatment (\textbf{Figure 3b} ). According to previous reports, the
higher degree of leakage caused by cold methanol may be partly
attributed to the following aspects. The first was the increase in cell
membrane fluidity or the decrease in thickness during the quenching
process {[}57{]}; The sudden increase in cell membrane permeability
caused by ''cold shock'' was also reported {[}58{]}; In addition,
methanol was a small molecule which was easier to penetrate cells which
may also be the reason for the leakage of intracellular metabolites
{[}22{]}. Moreover, Canelas et al. found that the extent of metabolite
leakage in\emph{S. cerevisiae} was affected by the methanol content of
the quencher, and reducing the methanol concentration resulted in the
increase of intracellular metabolite leakage {[}44{]}. Therefore, the
necessity of fine-tuning quenching strategy should be emphasized. In
general, it could be concluded that methanol quenching was not suitable
for accurate quantification of the metabolites in Hela cells. Normal
saline was the better of the two quenchers, although it could not
completely prevent leakage.

Furthermore, we investigated the factors such as the molecular weight
which has been reported to affect the degree of metabolite leakage
{[}56{]}. \textbf{Figure 4} showed the relationship between the degree
of metabolite leakage and molecular size which was represented by
molecular weight. We found that metabolites with a smaller molecular
weight were more seriously leaked than metabolites with a larger
molecular weight when normal saline was used as the quencher. This was
consistent with the findings by Canelas et al. observed in \emph{S.
cerevisiae} that the leakage of the metabolites into the quenching
solution was mostly driven by diffusion {[}56{]} and the diffusion rate
of smaller molecules was higher {[}44{]}. In contrast with this, when
cold methanol was used as the quencher, the degree of metabolite leakage
showed no clear relation with the molecular weight, and severe leakage
was observed to be widespread in the detected metabolites.

\emph{3.1.2 Metabolite coverage and abundance identified in} \emph{cell
extracts}

The comparison of quantitative whole-cell metabolites showed that the
number and total amount of metabolites extracted under each condition
were significantly different \textbf{(Figures 5 and 6)} . For example,
Oxaloacetic acid (OAA) was below the detection limit with
methanol/chloroform as the extractant, but detectable in all the other
extractants. Most sample preparation procedures frequently used cold
organic solvents to stop cellular metabolic activities. In this study,
we also quenched cellular metabolism directly by adding liquid
N\textsubscript{2} on the flask plate, followed by the addition of
extraction solvent. Our results showed relatively less metabolites were
obtained with methanol as the quencher compared to cells quenched with
normal saline or directly quenched with liquid N\textsubscript{2}.
Kapoore et al. also found that methanol quenching treatments resulted in
severe leakage of nearly all metabolite classes in metastatic breast
cancer cell line MDA-MB-231 {[}29{]}. In addition to this, the result
also showed that direct quenching with liquid N\textsubscript{2}produced
the highest recovery of all metabolites compared with methanol and
normal saline (\textbf{Figure 6} ). Additionally, the overall effect of
normal saline as the quenching agent was between liquid nitrogen and
methanol. Consistently, one previous study also reported that quenching
with 60\% methanol (buffered or unbuffered) resulted in leakage of
intracellular metabolites from the cells. Whereas, quenching with cold
normal saline (0.9\% {[}w/v{]} NaCl, 0.5\selectlanguage{english}) did not damage the cells and
effectively prevented the conversion of ATP to adenosine diphosphate
(ADP) and adenosine monophosphate (AMP), indicating metabolic arrest in
CHO cells {[}48{]}.

Furthermore, based on the number of metabolites extracted, acetonitrile
shows the best extraction efficiency (\textbf{Figure 5} ). In addition
to the total number of extracted metabolites, the total amount of
extracted metabolites was also an important indicator for evaluating the
extraction methods. The results showed that using liquid nitrogen as the
quencher and 50\% acetonitrile as the extractant, the maximum total
amount of intracellular metabolites could be harvested, about 295
nmol/million cells, which was 1.67 times to 13.73 times higher than
other combination methods. In addition, it could be observed that this
optimal method allowed a high extraction capacity for adenosine
nucleotides and coenzymes (\textbf{Figure 6} ). For the four different
extractants, it can be inferred that the extraction efficiency of
acetonitrile and ethanol was generally higher, while the extraction
efficiency of methanol: chloroform: water (1:1:1, v/v/v) (M/C) was the
worst regardless of the type of quenchers (\textbf{Figure 6} ).

\emph{3.1.3 Absolute quantitation of intracellular metabolite
concentrations}

The absolute concentration of the intracellular metabolites was used to
compare the efficiency of extraction solvents. We found that different
extraction solvents differed in extraction efficiencies of specific
intracellular metabolites. In terms of amino acid recovery, we analyzed
18 amino acids by GC-MS and observed a clear trend for amino acids. The
largest amount of amino acids was extracted when the liquid nitrogen as
the quenching agent and 50\% cold acetonitrile as the extractant.
However, the extraction of amino acids with cold methanol as the
quenching agent was very poor, which was caused by the serious leakage
of amino acids mentioned above. Also, methanol/chloroform (M/C) had a
poor extraction effect on intracellular amino acids. This may be
ascribed to the weak polarity of methanol/chloroform and low solubility
of amino acids. M/C extraction, which was originally used to extract
non-polar metabolites {[}59{]}, such as fatty acids {[}18{]}. Hot
ethanol extraction was proved to be as effective as acetonitrile for
amino acid recovery, but there were differences in the selective
recovery of specific amino acids such as serine (Ser), aspartate (Asp),
asparagine (Asn) (\textbf{Figure 7} ). Overall, amino acids were more
soluble in water-based extractions (methanol, acetonitrile, and hot
ethanol) which was consistent with previous study {[}26{]}.

For organic acids and sugar phosphates, cold methanol as the quencher
also behaved poor extraction effects and extracted the least amount. The
recovery of fumarate (FUM), oxaloacetate (OAA) and phosphoenolpyruvic
acid (PEP) was relatively low regardless of the type of extractants. For
the rest organic acids such as pyruvic acid (PYR), malate (MAL) and
\selectlanguage{greek}α-\selectlanguage{english}ketoglutarate (\selectlanguage{greek}α \selectlanguage{english}KG), using cold methanol and hot ethanol as the
extractant can achieve better extraction results. Among sugar
phosphates, the extraction effects of glyceraldehyde 3-phosphate (G3P),
3-phosphoglycerate (3PG), erythrose-4-phosphate (E4P) and
fructose-1,6-bisphosphate (FBP) were generally poor. When liquid
nitrogen and acetonitrile was used as the quencher and the extractant
respectively, the extraction amount of 6-phosphogluconate (6PG) and
sedoheptulose-7-phosphate (S7P) was the highest, and the extracted S7P
even reached about 5.89 nmol/million cells, which was 8.96 times to
295.69 times higher than other extraction methods. This method was also
suitable for the extraction of glucose-6-phosphate (G6P) and
fructose-6-phosphate (F6P) which was only slightly less than normal
saline quenching - 80\% methanol extraction (S-M) and liquid nitrogen
quenching - 80\% methanol extraction (N\textsubscript{2}-M)
(\textbf{Figure 8} ).

Finally, high temperature in some extraction methods might affect the
recovery of thermally unstable metabolites. Nevertheless, in our study,
we did not observe that hot ethanol obviously caused the adverse effect
regarding the number and amount of detected metabolites (\textbf{Figure
8} ). One previous study has reported that several metabolites (PYR,
nucleotides and sugar phosphates) were unstable in hot ethanol {[}22{]}.
However, Sellick et al. found that hot ethanol had a good performance on
recovering fatty acids such as stearic acid and palmitic acid because of
the increased solubility of fatty acids {[}26{]}. To demonstrate the
specific effects of heat extraction, we focused on the levels of the
labile metabolite nicotinamide adenine dinucleotide (NAD)
(\textbf{Figure 8} ). The recovery of intracellular NAD clearly showed
that liquid nitrogen quenching - 50\% acetonitrile extraction
(N\textsubscript{2}-A) and normal saline quenching - 50\% acetonitrile
extraction (S-A) recovered relatively larger amounts of NAD, hot ethanol
extraction was slightly inferior, but better than methanol and M/C as
extractants. In our study, M/C was poor in the extraction of each
metabolite although previous study showed certain advantages in the
extraction of cholesterol and fatty acids {[}57{]}. Therefore, these
data emphasized the importance of using the appropriate
quenching-extraction process for the extraction of specific metabolites.
An ideal extraction method should recover the metabolites as much as
possible; however, one extraction method in general was not capable of
allowing a complete metabolite extraction. For this purpose, a combined
extraction process might be an alternative to increase the number and
amount of metabolites. However, the reduction of accuracy and
reproducibility of the combination method in extraction was also an
undisputable fact, which largely offsets its advantage in enhancing the
metabolite spectrum {[}57{]}.

\emph{3.1.4 Reproducibility of metabolite quantification}

To evaluate the reproducibility of the methods detected, we used average
relative standard deviation (RSD) as an indicator to evaluate the
reproducibility of the extraction solvent. The RSD results of 43
metabolites obtained using 12 combinations of quenching and extraction
methods were shown in \textbf{Table 2} . The introduction
of\textsuperscript{13}C internal standards allowed us to quantitatively
evaluate extraction methods with regard to biological reproducibility.
In 2D Hela cells, the difference between the average RSD values of
different methods was not significant, mostly between 20\%-30\%. S-A
method had the lowest average RSD (18.9\%) while the
N\textsubscript{2}-M/C method had the largest average RSD (39.9\%). The
average RSD of the N\textsubscript{2}-A method which behaved excellent
regarding metabolite coverage and abundance was 27.8\%. The RSD values
of 11 methods (except N\textsubscript{2}-M/C) were below 30\%,
indicating good reproducibility independent of quenching and extraction
procedures. In addition, in all methods (except N\textsubscript{2}-M/C),
the percentage of individual metabolites with RSD \textless{}30\% was
higher than 58\% (\textbf{Table 3} ). Guenther et al. found that in
HCT116 cells, the percentages of metabolites with RSD \textless{} 30\%
cultured in 10\% and 20\% fetal calf serum (FCS) conditions using
different quenching solvents, extraction ratios were ranging from 0\% to
79\% which had a large error {[}20{]}. In our study, the percentage of
individual metabolites with RSD \textless{}30\% for the optimal
N\textsubscript{2}-A method could reach 61.0\%, and the largest
percentage of individual metabolites with RSD \textless{}30\% of all
methods was 69.9\%, which proved that our methods were reproducible for
most metabolites and N\textsubscript{2}-A method was relatively stable
for the extraction of metabolites detected. By analyzing the RSDs of 12
methods, we found that the sample preparation methods using liquid
N\textsubscript{2} as the quenching agent had higher RSDs than the other
two quenching agents, and the proportion of metabolites with RSDs less
than 30\% was also lower. We speculated that this might be caused by the
difficulty in accurately determining the added volume and the
volatilization time of liquid nitrogen, which made it impossible to
ensure complete consistency of the quenching time (the contact time
between samples and quenching agents) between parallel samples. However,
for the metabolites with RSDs>50\%, there were only three metabolites
in N\textsubscript{2}-A method, which was the fewest among all 12
methods.

\textbf{3.2 Validation of the optimal quenching - extraction procedure
on 3D MTSs}

3D tumor spheroids are important model systems due to the capability of
capturing in vivo tumor complexity. To our knowledge, few relevant
studies have ever reported comprehensive evaluation of intracellular
metabolite extraction of 3D MTSs. To date, only a few metabolomics
studies have been dealt with 3D MTS models {[}60, 61{]}. Despite showing
strong potential for combining 3D MTSs and metabolomics study, a
detailed protocol for obtaining absolute metabolome was far from
complete. For example, Rusz et al. used the HCT116 3D MTS model for the
experimental design of a metabolomics workflow. The established protocol
consisted of a quick wash of the spheroids on the plate followed by
extraction with cold methanol {[}51{]}. However, it was uncertain
whether this method was suitable for the extraction of intracellular
metabolites from 3D MTS in a leakage-free manner. In addition, this
protocol has not been quantitatively validated and also has not been
applied to 2D adherent cells for broad applicability. In this study,
sample preparation protocols developed with 2D monolayer cultures were
extended to acquire single spheroid metabolomics. We aimed to
investigate whether the best sample preparation protocol for 2D cells
was also applicable to 3D MTSs. The preparation method of tumor
spheroids followed the steps as described in \textbf{Material and
methods 2.2} . As the incubation time increased, the spheroid became
rounder and bigger (\textbf{Figure S2A} ). On the 6th day, the MTS
reached a plateau with a maximum diameter of 538.26 \selectlanguage{ngerman}± 7.19 \selectlanguage{greek}μ\selectlanguage{english}m, the
roundness of the MTSs exceeded 0.9, and the number of viable cells of a
single MTS reached the maximum, about 34000\selectlanguage{ngerman}±1000 (\textbf{Figure S2B} ).
The morphology and microstructure of the 6th day MTSs were observed with
a scanning electron microscope (SEM), and it was found that the MTSs had
a good 3D structure (\textbf{Figure S2C} ). After 6 days of culture,
tumor spheroids were collected and the intracellular metabolites were
obtained using the above-mentioned 12 combinations of quenching and
extraction methods.

The experimental results demonstrated that in 3D MTSs, the leakage of
intracellular metabolite with methanol as the quencher was more serious
than that of normal saline, which was the same as that found in 2D cells
(\textbf{Figure 9} ). Also, the same conclusion was drawn with 3D tumor
spheroid metabolomics analysis that the degree of metabolite leakage was
associated with molecular weight (\textbf{Figure S3} ). For the 12
quenching-extraction methods, N\textsubscript{2}-A method was also the
best method applied to 3D MTSs in terms of the number and amount of
intracellular metabolites extracted (\textbf{Figure S4} ). Moreover, the
percentage of intracellular metabolites with RSD less than 30\% in 3D
MTSs for the N\textsubscript{2}-A method can reach 48.7\% (\textbf{Table
S2} ). However, we found the average RSD values of each metabolite from
3D MTSs were significantly higher (P\selectlanguage{english}<0.01) than that from 2D cells as a
whole (\textbf{Figure S5} ), which might be ascribed to the fact that
some small cell clusters in single cell suspension (\textbf{Figure S6} )
have a certain impact on the reproducibility of metabolite extraction.
This might be solved by extending the enzymatic hydrolysis time to
reduce cell clusters, but long-time exposure to Accutase\selectlanguage{english} cell digestive
solution likely affects membrane integrity and permeability and causes
the leakage of intracellular metabolites, which needed to be verified by
subsequent experiments. Taken above, our study showed that the optimized
sample preparation protocol (N\textsubscript{2}-A method) developed for
the intracellular metabolite extraction of 2D adherent cancer cells can
be well applied to 3D MTSs.

\textbf{3.3 Assessing the intracellular metabolic changes of Hela cells
exposed to DOX}

The above results arrive at the conclusion that N\textsubscript{2}-A has
been identified as the most optimal method to acquire intracellular
metabolites with minimal loss during Hela carcinoma sample preparation.
Metabolomics is emerging as an important tool for understanding the
molecular mechanisms underlying the response of drug delivery systems.
Further, in this study, a case study with the acquired intracellular
metabolomics data from Hela carcinoma cell under both 2D monolayer
culture and 3D MTS in response to anticancer drug DOX treatment was
illustrated using this optimal sample preparation protocol. DOX is a
kind of anti-tumor antibiotics widely used in clinics and prevents the
resealing of DNA double helix and subsequently creates a double stranded
DNA break which leads to cell death {[}62-64{]}. In our study, we
focused on the intracellular metabolite changes of Hela cells exposed to
DOX. We determined the cytotoxicity of the drug to 2D monolayer culture
and 3D MTSs through cell counting kit-8 (CCK-8). As shown
in\textbf{Figure S7} , compared with the 2D control, 3D MTSs showed
higher resistance to DOX treatment. After 48 hours of DOX treatment, the
half maximal inhibitory concentration (IC\textsubscript{50}) values of
3D MTSs and 2D monolayer culture cells were 13.13 \selectlanguage{greek}μ\selectlanguage{english}M and 1.108 \selectlanguage{greek}μ\selectlanguage{english}M,
respectively. As expected, the result indicated that 3D Hela MTSs were
less sensitive to DOX than 2D monolayer cells.

As shown in \textbf{Figure 10} , extracellular concentrations of
glucose, lactic acid, glutamine and ammonia were measured. It has been
acknowledged that cancer cells rely on aerobic glycolysis instead of
mitochondrial respiration for the rapid provision of ATP and precursors,
a phenomenon termed ``the Warburg effect'' {[}65{]}. Apparently, 3D
cells consumed more glucose for aerobic glycolysis and produced a larger
amount of lactate as compared to 2D cells (\textbf{Figure 10} ).
Therefore, 3D MTSs conserved a more tumor metabolic phenotype in terms
of Warburg effect. After adding 1\selectlanguage{greek}μ\selectlanguage{english}M DOX in 2D cells, extracellular
glucose was hardly consumed, and the level of lactate remained basically
unchanged, which was around 0.2 g/L with the dosing time. By contrast,
the ability of 3D MTSs to consume glucose and produce lactic acid was
significantly stronger than 2D cells. This might indicate that the
addition of DOX had a greater impact on the respiration capacity of 2D
cells than 3D cells. In addition, it was found that when glucose and
glutamine were present at the same time, cancer cells preferably used
glutamine first. The addition of DOX did not change the utilization of
glutamine in both 2D and 3D cells. Regarding ammonia metabolism, the
ammonia produced by 2D cells was gradually accumulated, while 3D cells
accumulated ammonia in the early stage of culture, and re-consumed
ammonia in the later stage of culture. This seemed reasonable because
metabolic recycle of ammonia has been reported to favor tumor cell
growth {[}6{]}.

After the DOX treatment, the hierarchical cluster analysis of the
acquired intracellular metabolites showed that the treatment on 3D MTSs
elicited more pronounced metabolic changes than 2D monolayer cultures
(\textbf{Figure 11} ). Specifically, 2D monolayer cells before and after
dosing showed significant regulation on 13 metabolites among which G3P,
proline (Pro) and pyruvate (PYR) were upregulated while glycine (Gly),
ribose-5-phosphate (R5P), serine (Ser), nicotinamide adenine
dinucleotide phosphate (NADP), glutamine (Gln), NAD, cysteine (Cys),
aspartate (Asp), threonine (Thr), and malate (MAL) were down-regulated.
In contrast, 24 metabolites were significantly changed in 3D MTS after
dosing with DOX, where citrate (CIT), G3P, Ser, Cys, Ala, Thr,
glutamine, valine, and lysine were up-regulated while PYR, Glu, 3PG,
succinate (SUC), NAD, 6PG, NADP, leucine (Leu), AMP, ATP, AKG, Pro, Asn,
E4P, and PEP were down-regulated. Armi\selectlanguage{ngerman}ñán et al. found that 2D MCF7
cells exposed to free DOX reduced the intracellular levels of Gly, NAD
and Asp, which was consistent with our findings in 2D Hela cells
{[}66{]}. Previous studies have also shown that reduced levels of Gly
(2D) and Glu (3D) were associated with reduced glycolysis {[}67{]}, and
Gly in particular has been considered as a biomarker for cancer
prognosis and treatment response {[}68{]}. To corroborate this,
extracellular metabolite data revealed that glucose uptake and lactate
production were inhibited in both 2D and 3D cells after exposure to DOX,
which also indicated a reduced glycolysis (\textbf{Figure 10} ).
Interestingly, the intracellular Gln pool showed different trends in 2D
and 3D cells after dosing with DOX. Glutamine was an important source of
carbon in cells. Glutaminolysis was necessary for cancer cells to
satisfy the need to replenish TCA cycle intermediates and to produce
NADPH {[}69{]}. For 2D cells, the addition of DOX reduced the glycolytic
flux and the intracellular glutamine concentration, indicating that the
cells may be insufficient to replenish the TCA cycle through glutamine,
resulting in the death of 2D cells; while for 3D cells, intracellular
glutamine concentration was observed increased, indicating that
glutamine was sufficient to maintain the replenishment requirements of
the TCA cycle when the cells were reduced in glycolysis.

As shown in \textbf{Figure 12} , pathway enrichment analysis using
targeted metabolomics data showed that differential pathways for 2D and
3D cells after doses of DOX were associated with redox stress, such as
glutathione metabolism (2D:
Glycine/NADP\textsuperscript{+}/L-Cysteine/Acetyl-CoA/L-Glutamate
down-regulated, 3D: NADP\textsuperscript{+}/L-Glutamate down-regulated,
L-Cysteine up-regulated), purine metabolism (2D: D-Ribose
5-phosphate/L-Glutamine down-regulated, 3D: ADP/AMP/ATP down-regulated,
L-Glutamine up-regulated), nicotinate and nicotinamide metabolism (2D:
NADP\textsuperscript{+}/ L-Aspartate down-regulated, 3D:
NADP\textsuperscript{+}/NAD\textsuperscript{+} down-regulated).
Oxidative stress signifies the imbalance between the production of
reactive oxygen species (ROS) and antioxidant defenses, which could lead
to apoptosis and is accompanied by a lot of metabolic alterations.
Metabolic changes associated with redox stress, including downregulation
of Gly (2D) and ATP (3D), have been implicated in oxidative
stress-induced aging {[}70{]}. In addition, previous study has also
showed that there appeared to be a link between the reduction of NAD
levels and the induction of oxidative stress {[}71{]}. To verify this,
we also measured ROS of Hela cells (2D cells and 3D MTSs) before and
after dosing with DOX (\textbf{Figure S8} ). Our results were consistent
with the putative DOX targeting mechanism of action and the increase in
redox stress where ROS has been confirmed by previous studies as the
main cause of cytotoxicity {[}62{]}. In addition to this, the results
also showed that DOX exposure would significantly affect amino acid
metabolism-related pathways (Glycine, serine and threonine metabolism,
cysteine and methionine metabolism, arginine and proline metabolism, and
alanine, aspartate and glutamate metabolism) in both 2D and 3D cells.
Triba et al. observed the simultaneous decrease of glutamine and alanine
after DOX-treated in B16-F10 cells which was interpreted as a
drug-induced transient switch from glycolysis to oxidative
phosphorylation to supply ATP, and further initiate apoptosis {[}72{]}.
Previous studies have also reported that apoptosis might be associated
with increased levels of branched-chain amino acids (valine, leucine,
and isoleucine) and decreased levels of alanine {[}66{]}. Finally, the
metabolomics data of this study also indicated the common pathway
enrichment, which had been reported on other drugs, such as purine and
pyrimidine metabolism. For example, Rusz et al. found that exposure to
oxaliplatin affected purine metabolism and pyrimidine synthesis, which
was in accordance with the ribosome biogenesis stress recently proposed
as the primary cause for the cytotoxic effects {[}51{]}.

\textbf{4. Conclusion}

In this study, we systematically evaluated 12 combinations of quenching
and extraction methods for acquiring quantitative intracellular
metabolome with both 2D and 3D Hela carcinoma models based on the
isotope dilution mass spectrometry method. Our results showed that
liquid nitrogen as quenching agent and 50\% acetonitrile as extracting
agent was proven to be the most optimal method to acquire intracellular
metabolome for both 2D Hela and 3D MTSs. Based on this, a quantitative
metabolomics study showed that DOX exposure gave rise to pronounced
metabolite changes between 2D and 3D Hela carcinoma models. Strikingly,
our data suggested that compared to 2D cells the increased intracellular
glutamine level in 3D cells can benefit replenishing the TCA cycle when
the glycolysis was limited after dosing with DOX.

In summary, this study provides a well-established quenching and
extraction protocol for quantitative metabolome profiling of Hela
carcinoma cell under 2D and 3D cell culture conditions. Based on this,
quantitative time-resolved metabolite data can serve to the generation
of hypotheses on metabolic reprogramming to reveal its important role in
tumor development and treatment.

\textbf{Acknowledgement}

This work was financially sponsored by State Key Laboratory of
Bioreactor Engineering, Scientific Research Think Tank of Biological
Manufacturing Industry in Qingdao (QDSWZK202004), the Fundamental
Research Funds for the Central Universities (Grant No. JKJ01211515) and
the 111 project (Grant No. B18022).

\textbf{Conflict of Interest}

The authors declare no conflict of interest.

\textbf{Data availability statement:} Data available on request from the
authors

\textbf{5. References}

{[}1{]} Sung, H., Ferlay, J., Siegel, R. L., Laversanne, M.\emph{, et
al.} , Global cancer statistics 2020: GLOBOCAN estimates of incidence
and mortality worldwide for 36 cancers in 185 countries. \emph{CA: a
cancer journal for clinicians} 2021, \emph{71} , 209-249.

{[}2{]} Tomasetti, C., Li, L., Vogelstein, B., Stem cell divisions,
somatic mutations, cancer etiology, and cancer prevention.\emph{Science}
2017, \emph{355} , 1330-1334.

{[}3{]} Pavlova, N. N., Thompson, C. B., The emerging hallmarks of
cancer metabolism. \emph{Cell Metab} 2016, \emph{23} , 27-47.

{[}4{]} Cairns, R. A., Harris, I. S., Mak, T. W., Regulation of cancer
cell metabolism. \emph{Nat Rev Cancer} 2011, \emph{11} , 85-95.

{[}5{]} Patra, K. C., Wang, Q., Bhaskar, P. T., Miller, L.\emph{, et
al.} , Hexokinase 2 is required for tumor initiation and maintenance and
its systemic deletion is therapeutic in mouse models of
cancer.\emph{Cancer Cell} 2013, \emph{24} , 213-228.

{[}6{]} Spinelli, J. B., Yoon, H., Ringel, A. E., Jeanfavre, S.\emph{,
et al.} , Metabolic recycling of ammonia via glutamate dehydrogenase
supports breast cancer biomass. \emph{Science} 2017, \emph{358} ,
941-946.

{[}7{]} Gong, Y., Liu, Z., Yuan, Y., Yang, Z.\emph{, et al.} , PUMILIO
proteins promote colorectal cancer growth via suppressing p21. \emph{Nat
Commun} 2022, \emph{13} , 1-17.

{[}8{]} Wang, T.-H., Wu, C.-C., Huang, K.-Y., Leu, Y.-L.\emph{, et al.}
, Integrated Omics Analysis of Non-Small-Cell Lung Cancer Cells
Harboring the EGFR C797S Mutation Reveals the Potential of AXL as a
Novel Therapeutic Target in TKI-Resistant Lung Cancer. \emph{Cancers}
2020,\emph{13} , 111.

{[}9{]} Lubes, G., Goodarzi, M., GC--MS based metabolomics used for the
identification of cancer volatile organic compounds as
biomarkers.\emph{J Pharmaceut Biomed Analysis} 2018, \emph{147} ,
313-322.

{[}10{]} Mayers, J. R., Wu, C., Clish, C. B., Kraft, P.\emph{, et al.} ,
Elevation of circulating branched-chain amino acids is an early event in
human pancreatic adenocarcinoma development. \emph{Nat Med}
2014,\emph{20} , 1193-1198.

{[}11{]} Mayers, J. R., Torrence, M. E., Danai, L. V.,
Papagiannakopoulos, T.\emph{, et al.} , Tissue of origin dictates
branched-chain amino acid metabolism in mutant Kras-driven
cancers.\emph{Science} 2016, \emph{353} , 1161-1165.

{[}12{]} do Valle, Í. F., Menichetti, G., Simonetti, G., Bruno,
S.\emph{, et al.} , Network integration of multi-tumour omics data
suggests novel targeting strategies. \emph{Nat Commun} 2018, \emph{9} ,
1-10.

{[}13{]} Jain, M., Nilsson, R., Sharma, S., Madhusudhan, N.\emph{, et
al.} , Metabolite profiling identifies a key role for glycine in rapid
cancer cell proliferation. \emph{Science} 2012, \emph{336} , 1040-1044.

{[}14{]} Newman, A. C., Maddocks, O. D., Serine and functional
metabolites in cancer. \emph{Trends Cell Biol} 2017, \emph{27} ,
645-657.

{[}15{]} Tao, L., Zhou, J., Yuan, C., Zhang, L.\emph{, et al.} ,
Metabolomics identifies serum and exosomes metabolite markers of
pancreatic cancer. \emph{Metabolomics} 2019, \emph{15} , 1-11.

{[}16{]} Garcia-Bermudez, J., Baudrier, L., La, K., Zhu, X. G.\emph{, et
al.} , Aspartate is a limiting metabolite for cancer cell proliferation
under hypoxia and in tumours. \emph{Nat Cell Biol} 2018, \emph{20} ,
775-781.

{[}17{]} León, Z., García\selectlanguage{english}-Canaveras, J. C., Donato, M. T., Lahoz, A.,
Mammalian cell metabolomics: experimental design and sample
preparation.\emph{Electrophoresis} 2013, \emph{34} , 2762-2775.

{[}18{]} Dettmer, K., Nurnberger, N., Kaspar, H., Gruber, M. A.\emph{,
et al.} , Metabolite extraction from adherently growing mammalian cells
for metabolomics studies: optimization of harvesting and extraction
protocols. \emph{Anal Bioanal Chem} 2011, \emph{399} , 1127-1139.

{[}19{]} Bi, H., Krausz, K. W., Manna, S. K., Li, F.\emph{, et al.} ,
Optimization of harvesting, extraction, and analytical protocols for
UPLC-ESI-MS-based metabolomic analysis of adherent mammalian cancer
cells. \emph{Anal Bioanal Chem} 2013, \emph{405} , 5279-5289.

{[}20{]} Fritsche-Guenther, R., Bauer, A., Gloaguen, Y., Lorenz, M.,
Kirwan, J. A., Modified Protocol of Harvesting, Extraction, and
Normalization Approaches for Gas Chromatography Mass Spectrometry-Based
Metabolomics Analysis of Adherent Cells Grown Under High Fetal Calf
Serum Conditions. \emph{Metabolites} 2019, \emph{10} , 2.

{[}21{]} Weibel, K. E., Mor, J.-R., Fiechter, A., Rapid sampling of
yeast cells and automated assays of adenylate, citrate, pyruvate and
glucose-6-phosphate pools. \emph{Anal Biochem} 1974, \emph{58} ,
208-216.

{[}22{]} Liu, X., Wang, T., Sun, X., Wang, Z.\emph{, et al.} , Optimized
sampling protocol for mass spectrometry-based metabolomics in
Streptomyces. \emph{Bioresourc Bioprocess} 2019, \emph{6} , 1-12.

{[}23{]} Garcia-Canaveras, J. C., Lopez, S., Castell, J. V., Donato, M.
T., Lahoz, A., Extending metabolome coverage for untargeted metabolite
profiling of adherent cultured hepatic cells. \emph{Anal Bioanal
Chem}2016, \emph{408} , 1217-1230.

{[}24{]} Sellick, C. A., Hansen, R., Maqsood, A. R., Dunn, W. B.\emph{,
et al.} , Effective quenching processes for physiologically valid
metabolite profiling of suspension cultured mammalian cells. \emph{Anal
Chem} 2009, \emph{81} , 174-183.

{[}25{]} Jang, C., Chen, L., Rabinowitz, J. D., Metabolomics and isotope
tracing. \emph{Cell} 2018, \emph{173} , 822-837.

{[}26{]} Sellick, C. A., Knight, D., Croxford, A. S., Maqsood, A.
R.\emph{, et al.} , Evaluation of extraction processes for intracellular
metabolite profiling of mammalian cells: matching extraction approaches
to cell type and metabolite targets. \emph{Metabolomics} 2010, \emph{6}
, 427-438.

{[}27{]} Wamelink, M. M., Struys, E. A., Huck, J. H., Roos, B.\emph{, et
al.} , Quantification of sugar phosphate intermediates of the pentose
phosphate pathway by LC--MS/MS: application to two new inherited defects
of metabolism. \emph{J Chromat B} 2005, \emph{823} , 18-25.

{[}28{]} Liu, X., Sun, X., Wang, T., Zhang, X.\emph{, et al.} ,
Enhancing candicidin biosynthesis by medium optimization and pH stepwise
control strategy with process metabolomics analysis of Streptomyces
ZYJ-6.\emph{Bioprocess Biosyst Eng} 2018, \emph{41} , 1743-1755.

{[}29{]} Kapoore, R. V., Coyle, R., Staton, C. A., Brown, N. J.,
Vaidyanathan, S., Influence of washing and quenching in profiling the
metabolome of adherent mammalian cells: a case study with the metastatic
breast cancer cell line MDA-MB-231. \emph{Analyst} 2017, \emph{142} ,
2038-2049.

{[}30{]} Varga, E., Glauner, T., Berthiller, F., Krska, R.\emph{, et
al.} , Development and validation of a (semi-) quantitative UHPLC-MS/MS
method for the determination of 191 mycotoxins and other fungal
metabolites in almonds, hazelnuts, peanuts and pistachios. \emph{Anal
Bioanal Chem} 2013, \emph{405} , 5087-5104.

{[}31{]} Buescher, J. M., Moco, S., Sauer, U., Zamboni, N., Ultrahigh
performance liquid chromatography\selectlanguage{english}- tandem mass spectrometry method for
fast and robust quantification of anionic and aromatic
metabolites.\emph{Anal Chem} 2010, \emph{82} , 4403-4412.

{[}32{]} Mashego, M., Wu, L., Van Dam, J., Ras, C.\emph{, et al.} ,
MIRACLE: mass isotopomer ratio analysis of U\selectlanguage{english}-13C\selectlanguage{english}-labeled extracts. A new
method for accurate quantification of changes in concentrations of
intracellular metabolites. \emph{Biotechnol Bioeng} 2004, \emph{85} ,
620-628.

{[}33{]} Wu, L., Mashego, M. R., van Dam, J. C., Proell, A. M.\emph{, et
al.} , Quantitative analysis of the microbial metabolome by isotope
dilution mass spectrometry using uniformly 13C-labeled cell extracts as
internal standards. \emph{Analy Biochem} 2005, \emph{336} , 164-171.

{[}34{]} Badea, M. A., Balas, M., Hermenean, A., Ciceu, A.\emph{, et
al.} , Influence of Matrigel on Single- and Multiple-Spheroid Cultures
in Breast Cancer Research. \emph{SLAS Discov} 2019, \emph{24} , 563-578.

{[}35{]} Lin, R. Z., Chang, H. Y., Recent advances in three-dimensional
multicellular spheroid culture for biomedical research. \emph{Biotechnol
J} 2008, \emph{3} , 1172-1184.

{[}36{]} Hirschhaeuser, F., Menne, H., Dittfeld, C., West, J.\emph{, et
al.} , Multicellular tumor spheroids: an underestimated tool is catching
up again. \emph{J Biotechnol} 2010, \emph{148} , 3-15.

{[}37{]} Mehta, G., Hsiao, A. Y., Ingram, M., Luker, G. D., Takayama,
S., Opportunities and challenges for use of tumor spheroids as models to
test drug delivery and efficacy. \emph{J Control Release}
2012,\emph{164} , 192-204.

{[}38{]} Stewart, D. J., Mechanisms of resistance to cisplatin and
carboplatin. \emph{Crit Rev Oncol Hemat} 2007, \emph{63} , 12-31.

{[}39{]} Costa, E. C., de Melo-Diogo, D., Moreira, A. F., Carvalho, M.
P., Correia, I. J., Spheroids Formation on Non-Adhesive Surfaces by
Liquid Overlay Technique: Considerations and Practical
Approaches.\emph{Biotechnol J} 2018, \emph{13} , 1700417.

{[}40{]} Wang, G., Chu, J., Zhuang, Y., van Gulik, W., Noorman, H., A
dynamic model-based preparation of uniformly-13C-labeled internal
standards facilitates quantitative metabolomics analysis of Penicillium
chrysogenum. \emph{J Biotechnol} 2019, \emph{299} , 21-31.

{[}41{]} Canelas, A. B., ten Pierick, A., Ras, C., Seifar, R. M.\emph{,
et al.} , Quantitative evaluation of intracellular metabolite extraction
techniques for yeast metabolomics. \emph{Anal Chem} 2009, \emph{81} ,
7379-7389.

{[}42{]} Wordofa, G. G., Kristensen, M., Schrubbers, L., McCloskey,
D.\emph{, et al.} , Quantifying the metabolome of Pseudomonas
taiwanensis VLB120: evaluation of hot and cold combined
quenching/extraction approaches. \emph{Anal Chem} 2017, \emph{89} ,
8738-8747.

{[}43{]} de Koning, W., van Dam, K., A method for the determination of
changes of glycolytic metabolites in yeast on a subsecond time scale
using extraction at neutral pH. \emph{Anal Biochem} 1992, \emph{204} ,
118-123.

{[}44{]} Canelas, A. B., Ras, C., Ten Pierick, A., van Dam, J. C.\emph{,
et al.} , Leakage-free rapid quenching technique for yeast
metabolomics.\emph{Metabolomics} 2008, \emph{4} , 226-239.

{[}45{]} Winder, C. L., Dunn, W. B., Schuler, S., Broadhurst, D.\emph{,
et al.} , Global metabolic profiling of Escherichia coli cultures: an
evaluation of methods for quenching and extraction of intracellular
metabolites. \emph{Anal Chem} 2008, \emph{80} , 2939-2948.

{[}46{]} Munger, J., Bajad, S. U., Coller, H. A., Shenk, T., Rabinowitz,
J. D., Dynamics of the cellular metabolome during human cytomegalovirus
infection. \emph{PLoS Pathog} 2006, \emph{2} , e132.

{[}47{]} Teng, Q., Huang, W., Collette, T. W., Ekman, D. R., Tan, C., A
direct cell quenching method for cell-culture based
metabolomics.\emph{Metabolomics} 2009, \emph{5} , 199-208.

{[}48{]} Dietmair, S., Timmins, N. E., Gray, P. P., Nielsen, L. K.,
Kromer, J. O., Towards quantitative metabolomics of mammalian cells:
development of a metabolite extraction protocol. \emph{Anal
Biochem}2010, \emph{404} , 155-164.

{[}49{]} Lorenz, M. A., Burant, C. F., Kennedy, R. T., Reducing time and
increasing sensitivity in sample preparation for adherent mammalian cell
metabolomics. \emph{Anal Chem} 2011, \emph{83} , 3406-3414.

{[}50{]} Lane, A. N., Fan, T. W.-M., Quantification and identification
of isotopomer distributions of metabolites in crude cell extracts using
1 H TOCSY. \emph{Metabolomics} 2007, \emph{3} , 79-86.

{[}51{]} Rusz, M., Rampler, E., Keppler, B. K., Jakupec, M. A.,
Koellensperger, G., Single Spheroid Metabolomics: Optimizing Sample
Preparation of Three-Dimensional Multicellular Tumor
Spheroids.\emph{Metabolites} 2019, \emph{9} , 304.

{[}52{]} Dubuis, S., Baenke, F., Scherbichler, N., Alexander, L.
T.\emph{, et al.} , Metabotypes of breast cancer cell lines revealed by
non-targeted metabolomics. \emph{Metab Eng} 2017, \emph{43} , 173-186.

{[}53{]} Zhang, K., Zhang, X., Bai, Y., Yang, L.\emph{, et al.} ,
Optimization of the sample preparation method for adherent cell
metabolomics based on ultra-performance liquid chromatography coupled to
mass spectrometry. \emph{Anal Methods} 2019, \emph{11} , 3678-3686.

{[}54{]} Kapoore, R. V., Coyle, R., Staton, C. A., Brown, N. J.,
Vaidyanathan, S., Cell line dependence of metabolite leakage in
metabolome analyses of adherent normal and cancer cell
lines.\emph{Metabolomics} 2015, \emph{11} , 1743-1755.

{[}55{]} Rushing, B. R., Schroder, M., Sumner, S. C., Comparison of
Lysis and Detachment Sample Preparation Methods for Cultured
Triple-Negative Breast Cancer Cells Using UHPLC--HRMS-Based
Metabolomics. \emph{Metabolites} 2022, \emph{12} , 168.

{[}56{]} de Jonge, L. P., Douma, R. D., Heijnen, J. J., van Gulik, W.
M., Optimization of cold methanol quenching for quantitative
metabolomics of Penicillium chrysogenum. \emph{Metabolomics}
2012,\emph{8} , 727-735.

{[}57{]} Shin, M. H., Lee, D. Y., Liu, K.-H., Fiehn, O., Kim, K. H.,
Evaluation of sampling and extraction methodologies for the global
metabolic profiling of Saccharophagus degradans. \emph{Anal Chem}
2010,\emph{82} , 6660-6666.

{[}58{]} Carnicer, M., Canelas, A. B., Ten Pierick, A., Zeng, Z.\emph{,
et al.} , Development of quantitative metabolomics for Pichia
pastoris.\emph{Metabolomics} 2012, \emph{8} , 284-298.

{[}59{]} Floch, J., A simple method for the isolation and purification
of total lipids from animal tissues. \emph{J. biol. Chem.}
1957,\emph{226} , 497-509.

{[}60{]} Fan, T. W., El-Amouri, S. S., Macedo, J. K. A., Wang, Q.
J.\emph{, et al.} , Stable Isotope-Resolved Metabolomics Shows Metabolic
Resistance to Anti-Cancer Selenite in 3D Spheroids versus 2D Cell
Cultures. \emph{Metabolites} 2018, \emph{8} , 40.

{[}61{]} Vorrink, S. U., Ullah, S., Schmidt, S., Nandania, J.\emph{, et
al.} , Endogenous and xenobiotic metabolic stability of primary human
hepatocytes in long\selectlanguage{english}-term 3D spheroid cultures revealed by a combination
of targeted and untargeted metabolomics. \emph{FASEB J} 2017, \emph{31}
, 2696-2708.

{[}62{]} Roychoudhury, S., Kumar, A., Bhatkar, D., Sharma, N. K.,
Molecular avenues in targeted doxorubicin cancer therapy. \emph{Future
Oncol} 2020, \emph{16} , 687-700.

{[}63{]} Mizutani, H., Tada-Oikawa, S., Hiraku, Y., Kojima, M.,
Kawanishi, S., Mechanism of apoptosis induced by doxorubicin through the
generation of hydrogen peroxide. \emph{Life Sci} 2005, \emph{76} ,
1439-1453.

{[}64{]} Lorenzo, E., Ruiz-Ruiz, C., Quesada, A. J., Hernandez,
G.\emph{, et al.} , Doxorubicin induces apoptosis and CD95 gene
expression in human primary endothelial cells through a p53-dependent
mechanism. \emph{J Biol Chem} 2002, \emph{277} , 10883-10892.

{[}65{]} Jang, M., Kim, S. S., Lee, J., Cancer cell metabolism:
implications for therapeutic targets. \emph{Exp Mol Med} 2013,\emph{45}
, e45.

{[}66{]} Arminan, A., Palomino-Schatzlein, M., Deladriere, C.,
Arroyo-Crespo, J. J.\emph{, et al.} , Metabolomics facilitates the
discrimination of the specific anti-cancer effects of free-and
polymer-conjugated doxorubicin in breast cancer
models.\emph{Biomaterials} 2018, \emph{162} , 144-153.

{[}67{]} Yang, Y., Li, C., Nie, X., Feng, X.\emph{, et al.} ,
Metabonomic studies of human hepatocellular carcinoma using
high-resolution magic-angle spinning 1H NMR spectroscopy in conjunction
with multivariate data analysis. \emph{J Proteome Res} 2007, \emph{6} ,
2605-2614.

{[}68{]} Mirbahai, L., Wilson, M., Shaw, C. S., McConville, C.\emph{, et
al.} , 1H magnetic resonance spectroscopy metabolites as biomarkers for
cell cycle arrest and cell death in rat glioma cells. \emph{Int J
Biochem \& Cell Biol} 2011, \emph{43} , 990-1001.

{[}69{]} DeBerardinis, R. J., Sayed, N., Ditsworth, D., Thompson, C. B.,
Brick by brick: metabolism and tumor cell growth. \emph{Current Opini
Genet Develop} 2008, \emph{18} , 54-61.

{[}70{]} Zeng, J., Liu, J., Yang, G.-Y., Kelly, M. J.\emph{, et al.} ,
Exogenous ethyl pyruvate versus pyruvate during metabolic recovery after
oxidative stress in neonatal rat cerebrocortical slices. \emph{Journa
American Society of Anesthesiologists} 2007, \emph{107} , 630-640.

{[}71{]} Klawitter, J., Klawitter, J., Gurshtein, J., Corby, K.\emph{,
et al.} , Bezielle (BZL101)\selectlanguage{english}-induced oxidative stress damage followed by
redistribution of metabolic fluxes in breast cancer cells: A combined
proteomic and metabolomic study. \emph{Int J Cancer} 2011, \emph{129} ,
2945-2957.

{[}72{]} Triba, M., Starzec, A., Bouchemal, N., Guenin, E.\emph{, et
al.} , Metabolomic profiling with NMR discriminates between
biphosphonate and doxorubicin effects on B16 melanoma cells. \emph{NMR
Biomed} 2010,\emph{23} , 1009-1016.

\textbf{Table 1} . Reported sample preparation procedures for
metabolomics analysis of cultured mammalian cells.\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
\textbf{Cell type} & \textbf{Rinse} & \textbf{Quenching} &
\textbf{Extraction} & \textbf{Analytical technique} &
\textbf{Ref.}\tabularnewline
\midrule
\endhead
\textbf{Human rhabdomyosarcoma cells} & Trypsinization/three times with
ice-cold PBS & Liquid nitrogen & 10\% ice-cold TCA & Nuclear magnetic
resonance (NMR) & {[}50{]}\tabularnewline
\textbf{Fibroblasts} & No & Methanol: water 80:20 (80\% methanol) at -75
\selectlanguage{ngerman}°C & Methanol: water 80:20 (80\% methanol) at -75 \selectlanguage{ngerman}°C & LC-MS/MS &
{[}46{]}\tabularnewline
\textbf{CHO} & No & 60\% methanol at -40 \selectlanguage{ngerman}°C & 100\% methanol & Gas
chromatography/time of flight-mass spectrometry (GC-TOF-MS) &
{[}24{]}\tabularnewline
\textbf{MCF-7 and MDA-MB-231 cells} & Twice with ice-cold
phosphate-buffered saline (PBS, pH 7.4) & High Performance Liquid
Chromatography (HPLC) grade methanol & A mixture of methanol, chloroform
and water in the volume ratio of 4:4:2.85 & NMR &
{[}47{]}\tabularnewline
\textbf{HCT116} & Once with PBS & Liquid nitrogen & Cold 80\% methanol &
LC-MS & {[}51{]}\tabularnewline
\textbf{HCT116} & Washed (20 s) with washing buffer (140 mM NaCl, 5 mM
HEPES, pH 7.4, 37 \selectlanguage{ngerman}°C) & Ice-cold 50\% MeOH solution & 1:1:1, v/v/v MeOH:
CHCl\textsubscript{3}: H2O & GC-MS & {[}20{]}\tabularnewline
\textbf{Breast cancer cell lines} & Washed twice with a washing solution
(75 mM ammonium carbonate, adjusted to pH 7.4 with Acetic Acid) & Liquid
nitrogen & 70\% (v: v) ethanol: water at 75 \selectlanguage{ngerman}°C & Flow injection analysis
time-of-flight mass spectrometry & {[}52{]}\tabularnewline
\textbf{CHO} & No & 60\% methanol supplemented with 0.85\% (w/v) AMBIC
(pH 7.4) at -40\selectlanguage{ngerman}°C & Methanol + water & GC-MS & {[}26{]}\tabularnewline
\textbf{SW480} & No & Liquid nitrogen & 80\% methanol, 20\% water
(MeOH/H2O) & GC-MS & {[}18{]}\tabularnewline
\selectlanguage{greek}\textbf{Clonal β-cell line INS-1}\selectlanguage{english} & 37 \selectlanguage{ngerman}°C deionized water (\selectlanguage{english}~2 s) &
liquid nitrogen & 9:1 methanol: chloroform & HPLC-MS &
{[}49{]}\tabularnewline
\textbf{Recombinant CHO cell line Super-CHO} & Washed twice with PBS at
room temperature & Cold 0.9\% (w/v) NaCl & Cold extraction using 50\%
acetonitrile & HPLC & {[}48{]}\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 2} The average RSD of the intracellular metabolites of 2D
Hela cells using 12 combinations of quenching and extraction methods.\selectlanguage{english}
\begin{longtable}[]{@{}lllllllllllll@{}}
\toprule
\textbf{Methods} & \textbf{N\textsubscript{2}-M} &
\textbf{N\textsubscript{2}-A} & \textbf{N\textsubscript{2}-M/C} &
\textbf{N\textsubscript{2}-E} & \textbf{M-M} & \textbf{M-A} &
\textbf{M-M/C} & \textbf{M-E} & \textbf{S-M} & \textbf{S-A} &
\textbf{S-M/C} & \textbf{S-E}\tabularnewline
\midrule
\endhead
\textbf{Average RSD (\%)} & 27.5 & 27.8 & 39.9 & 20.2 & 24.5 & 26.4 &
26.2 & 24.4 & 25.5 & 18.9 & 26.0 & 22.4\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 3} The percentage of intracellular metabolites with RSD
less than 30\% in 2D Hela cells using 12 combinations of quenching and
extraction methods.\selectlanguage{english}
\begin{longtable}[]{@{}lllllllllllll@{}}
\toprule
\textbf{Methods} & \textbf{N\textsubscript{2}-M} &
\textbf{N\textsubscript{2}-A} & \textbf{N\textsubscript{2}-M/C} &
\textbf{N\textsubscript{2}-E} & \textbf{M-M} & \textbf{M-A} &
\textbf{M-M/C} & \textbf{M-E} & \textbf{S-M} & \textbf{S-A} &
\textbf{S-M/C} & \textbf{S-E}\tabularnewline
\midrule
\endhead
\textbf{RSD (\%) <30} & 58.5 & 61.0 & 39.0 & 78.0 & 75.6 & 73.2 & 68.3
& 75.6 & 73.2 & 82.9 & 73.2 & 73.2\tabularnewline
\bottomrule
\end{longtable}

\textbf{Figure legends}

\textbf{Figure 1} Schematic diagram of the sampling, quenching and
extraction procedures for Hela cells from 2D monolayer cultures and 3D
MTS.

\textbf{Figure 2} Parameters evaluated during the optimization of
metabolome extraction and analysis conditions. The cell collection
procedure remains constant for all different combinations of metabolome
quenching, extraction and GC/LC-MS analysis. Metabolic quenching is
achieved by adding quenchers to the cells. Then, cell separation and
metabolite extraction are performed at the same time by scraping the
cells in different extraction solvents.

\textbf{Figure 3} Comparison of the leakage degree of metabolites in
different quenchers. (a) The leakage degree of amino acids in different
quenchers; (b) The leakage degree of organic acids, phosphate sugars,
adenosine nucleotide and coenzymes in different quenchers. Values were
the mean of three biological replicates. Error bars represented standard
deviation.

\textbf{Figure 4} The relationship between the degree of leakage and
molecular weight in 2D cells. The degree of leakage was calculated based
on the level of metabolites in the quencher and the overall metabolite
level (quencher + extractant).

\textbf{Figure 5} The total number of metabolites obtained by each
extraction method.

\textbf{Figure 6} The sum of the different kind metabolites identified
in cell extracts only in 2D cells. Significant analysis was performed
against N\textsubscript{2}-A group.

\textbf{Figure 7} Eighteen amino acids were identified in 12
quenching-extraction methods. Values were the mean of three biological
replicates. Error bars represented standard deviation.

\textbf{Figure 8} Eight organic acids, nine sugar phosphates, eight
adenosine nucleotides and coenzymes were identified in twelve extraction
methods. Values were the mean of three biological replicates. Error bars
represented standard deviation.

\textbf{Figure 9} The application of 12 intracellular extraction methods
on 3D MTSs. (A) Heat map of the degree of leakage of the two quenchers
in 3D MTSs; (B) 43 metabolites were identified in twelve extraction
methods in 3D MTSs.

\textbf{Figure 10} The measured concentrations of extracellular:
glucose, glutamine, ammonium and lactate. The culture time of 2D cells
and 3D MTSs was shown on the bottom and top axis, respectively.

\textbf{Figure 11} Heat map of the absolute amount of metabolites. The
2D/3D cells were treated with a medium containing 1 \selectlanguage{greek}μ\selectlanguage{english}M of DOX for 48 h.

\textbf{Figure 12} Kyoto Encyclopedia of Genes and Genomes (KEGG)
pathways affected by DOX using pathway enrichment with the MetaboAnalyst
pathway analysis module.\selectlanguage{english}
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