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Article
Simultaneous Extraction of RNA and Metabolites from Single KidneyTissue Specimens for Combined Transcriptomic and Metabolomic Profiling
Patrick Leuthold, Matthias Schwab, Ute Hofmann, Stefan Winter, Steffen Rausch,Michael N. Pollak, Jörg Hennenlotter, Jens Bedke, Elke Schaeffeler, and Mathias Haag
J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.8b00199 • Publication Date (Web): 09 Aug 2018
Downloaded from http://pubs.acs.org on August 16, 2018
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Simultaneous Extraction of RNA and Metabolites from Single Kidney Tissue Specimens for Combined Transcriptomic and Metabolomic
Profiling
Patrick Leuthold1, Matthias Schwab
1,4,5, Ute Hofmann
1, Stefan Winter
1, Steffen Rausch
1,2,Michael N. Pollak
3,
Jörg Hennenlotter2, Jens Bedke
2, Elke Schaeffeler
1+ , Mathias Haag
1+*
(1) Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany and University of
Tübingen, Tübingen, Germany
(2) Department of Urology, University Hospital Tübingen, Tübingen, Germany
(3) Jewish General Hospital, Montreal, QC, Canada
(4) Department of Clinical Pharmacology, University Hospital Tübingen, Tübingen, Germany
(5) Department of Pharmacy and Biochemistry, University of Tübingen, Tübingen, Germany
+Elke Schaeffeler and Mathias Haag contributed equally.
*Corresponding Author:
Dr. rer. nat. Mathias Haag
Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology
Auerbachstr. 112
70376 Stuttgart, Germany
Phone +49 (0)711 / 8101-5429
Fax +49 (0)711 / 85 92 95
Mail: [email protected]
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ABSTRACT
Tissue analysis represents a powerful tool for the investigation of disease pathophysiology.
However, the heterogeneous nature of tissue samples, in particular of neoplastic, may affect the
outcome of such analysis and hence obscure interpretation of results. Thus comprehensive
isolation and extraction of transcripts and metabolites from an identical tissue specimen would
minimize variations and enable the economic use of biopsy material which is usually available in
limited amounts. Here we demonstrate a fast and simple protocol for combined transcriptomics
and metabolomics analysis in homogenates prepared from one single tissue sample.
Metabolites were recovered by protein precipitation from lysates originally prepared for RNA
isolation and were analyzed by LC-QTOF-MS after HILIC and RPLC separation, respectively.
Strikingly, although ion suppression was observed, over 80% of the 2885 detected metabolic
features could be extracted and analyzed with high reproducibility (CV ≤ 20%). Moreover fold
changes of different tumor and nontumor kidney tissues were correlated to an established
metabolomics protocol and revealed a strong correlation (rp ≥ 0.75). In order to demonstrate the
feasibility of the combined analysis of RNA and metabolites, the protocol was applied to kidney
tissue of metformin treated mice to investigate drug induced alterations.
Keywords: RNA, metabolites, lipids, metabolomics, transcriptomics, mass spectrometry,
combined transcriptomics and metabolomics
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INTRODUCTION
Merging tissue-derived transcriptomic and metabolic data has great potential to yield insight into
localized pathophysiological processes and to reveal new connections between gene expression
signatures and associated metabolites. For example, the joint analysis of transcripts and
metabolites has enabled to uncover an increase in gene-metabolite coupling in breast cancer
and hepatocellular tumors 1. Furthermore, it was shown that metabolite levels can be predicted
from transcriptome data. Another study showed that, through integrative transcriptome and
metabolome profiling, novel functional roles for genes can be uncovered which determines
tissue-specific metabolism 2. By applying systematic metabotyping via H NMR spectroscopy and
genome-wide gene expression analysis in white adipose tissue from type 2 diabetic rats, the
authors revealed specific genetic polymorphisms that are associated with cellular metabolites
such as glucose and 3-hydroxybutyrate. Moreover, also with respect to the therapeutic
mechanism of putative anticancer drugs (e.g. metformin), a combined metabolomic and
transcriptomic study has recently revealed time-dependent effects on the proliferation of colon
cancer cells implicating that energy metabolism may be one of the main targets of the drug 3.
In nephrology research, disease phenotyping by metabolomics has been applied increasingly in
particular to support kidney precision medicine 4. More specifically, regarding clear cell renal cell
carcinoma (ccRCC), the integration of transcriptomic and metabolomic data revealed, that
changes in metabolite and RNA expression levels are often asynchronous in ccRCC 5,6.
Moreover in order to study metabolic reprogramming in kidney cancer, Wettersten et al. showed
a combined metabolomics and proteomics approach of grade-dependent RCC samples 7.
However these studies are based on transcriptomics/proteomics and metabolomics analyses on
separate tissue pieces and a systematic study, e.g. providing RNA and metabolite extraction
from a single tissue piece is still lacking. Since heterogeneity in renal cancer is a major concern,
potentially resulting in clinically-relevant consequences like treatment decision 8, novel
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approaches that enable to capture comprehensive molecular information from a single tissue
sample are required.
The isolation of both RNA and metabolites in human tissue can be challenging as such material
is often available in limited amounts. Moreover, as tissue samples, in particular tumorous
material, are heterogeneous in nature, tumor purity (i.e. extend of stroma and lymphocyte
infiltration) can affect the outcome of genomic 9,10 as well as metabolomics analyses. This aspect
is even more critical if integration of data from different omics disciplines is envisioned in a
system-wide approach hence raising the demand to obtain “multi-omics data” from a single
piece of tissue rather than from replicate sample aliquots. Such a combined analysis offers
several advantages including economic use of biological material and minimization of
inconsistencies introduced through the analysis of different sample aliquots. As a consequence
more reliable functional transcriptome-metabolome relationships may be revealed.
Regarding RNA isolation, one of the most applied protocols is the single-step method from
Chomczynski et al. using reagents containing guanidinum thiocyanate and phenol-chloroform
mixtures 11,12, which has been successfully commercialized. Adaptions of such commercially
available DNA and RNA purification kits led to the development of innovative protocols for the
simultaneous isolation of DNA, RNA, miRNA and proteins from a single tissue specimen 13.
However, to our knowledge, the simultaneous analysis of metabolites and RNA in lysates
obtained from commercially available nucleic acid purification kits has not been considered
previously.
We therefore aimed to establish a protocol for the combined analysis of transcripts and
metabolites. Importantly, the protocol can be used without modifications for RNA purification and
thus enables the recovery of high quality transcripts. Metabolites can be recovered from tissue
lysates after protein precipitation in a highly reproducible manner. Besides sample
reproducibility, analytical reproducibility is not compromised by constituents of the
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homogenization buffer and only moderate ion suppression was observed. Furthermore the novel
protocol was compared to a recently published nontargeted metabolomics method 14 regarding
its capability to separate tumor and nontumor kidney tissue samples based on unsupervised
statistical analysis. As proof of concept the protocol was applied to kidney tissue of metformin-
treated mice in order to demonstrate the strength of combined transcriptomics/metabolomics
analyses to narrow down specific drug-induced pathway alterations.
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MATERIALS AND METHODS
Materials
Ultra LC-MS grade acetonitrile (ACN) and methanol (MeOH) were purchased from Carl Roth
GmbH & Co KG (Karlsruhe, Germany). Pure water was generated from a Milli-Q system
(Millipore, Billerica, MA, USA). 5mM purine dissolved in acetonitrile and 2.5 mM HP-0921
[Hexakis(1H,1H,3H-tetrafluoropropoxy)phosphazene] dissolved in acetonitrile was purchased
from Agilent Technologies (Waldbronn, Germany). Lysing buffer was obtained from the
mirVanaTM miRNA Isolation Kit (Ambion, Life Technologies, Darmstadt, Germany).
Tissue samples
Porcine kidney was obtained as commercially available fresh food product. Human kidney
samples were obtained after surgery from the University of Tübingen, informed written consent
was provided by each subject and the use of the tissue was approved by the ethics committee of
the University of Tübingen, Germany. The histological evaluation of the human tissue sections,
was performed at the Department of Pathology, University Hospital Tübingen, Germany. Mice
samples were obtained from the McGill University and the use of the tissue was approved by the
ethics committee of the McGill University, Canada. Three healthy male mice (Mus musculus,
strain C57BL/6) were administrated with 200mg/kg metformin via intraperitoneal injection and
the kidney was removed 1.5 h after the administration. Kidney tissue of three control healthy
mice was obtained in parallel. Upon resection, all kidney tissue samples were snap-frozen in
liquid nitrogen and were stored at -80 °C until sample homogenization.
Kidney tissue samples were prepared and analyzed in four different batches for the independent
assessment of reproducibility, method comparability and application. Batch A was used for the
reproducibility assessment and contained technical replicates of porcine kidney (n=10). Batch
B1 was used for method comparability and contained human tumor, diseased and benign kidney
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tissue samples from patients who underwent radical nephrectomy suffering from different forms
of kidney diseases such as clear cell renal cell carcinoma (ccRCC, tumor area), oncocytoma
(OC, tumor area) and urothelial cell carcinoma (UCC, benign area). For each kidney tissue
entity, technical replicates were collected (n=3). Batch B2 was also used for method
comparability and represents a subset of a sample batch that was prepared and analyzed
previously 14 and contained amongst others technical replicates (n=6) of the same donor
specimens (ccRCC, OC and benign UCC kidney tissue) as described for Batch B1. Batch C was
used for method application and consisted of kidney tissues of three control and, three
metformin treated mice.
Kidney tissue homogenization and metabolite extraction
The following procedure describes the sample preparation for the batches A, B1 and C. Batch
B2 was prepared as described 14. Frozen kidney samples of approximately 4-50 mg were
transferred to 2ml homogenization tubes containing 1.4 mm ceramic spheres (Lysing Matrix D,
MP Biomedicals, Heidelberg, Germany) prefilled with 600 µL of cold (4°C) lysing buffer derived
from the mirVanaTM miRNA Isolation Kit. Exact buffer composition is a company secret, however
commercialized buffers are adapted from widely used protocols 11,12 that follow the principle of
RNA isolation by using an acidic solution containing guanidinium thiocyanate, sodium acetate,
phenol and chloroform. Samples were homogenized in a FastPrep-24TM instrument (MP
Biomedicals, Heidelberg, Germany) at 7°C. Homogenization was achieved within three cycles
(6.5 m/s for 20 seconds) followed by weight determination of the homogenate. Samples were
centrifuged for 10 minutes (min) at 9391 x g and 4°C (Centrifuge type: 5424R, Eppendorf,
Germany) and the supernatants were separated into two aliquots of 300 µL in 1.5 mL
polypropylene tubes (Eppendorf, Germany). One aliquot was stored at -20°C as backup or for
transcriptome analysis. The other aliquot was pre-normalized according to weight (Batch A and
B1) or total RNA amount (Batch C) by addition of cold (4°C) lysing buffer to achieve the same
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solvent/tissue ratio for each sample in an analysis-batch. Samples were vortexed for 30 seconds
and from each sample an aliquot was transferred to a 1.5 mL polypropylene tube, followed by
the addition of acetonitrile (4 times the volume of the aliquot). The samples were vortexed for 30
seconds, incubated for 1h at -20°C for protein precipitation and centrifuged for 10 min at 21130 x
g at 4°C. For quality control (QC) sample preparation, aliquots of 100 µL from the supernatant of
each sample were pooled in a new 1.5 mL polypropylene tube and vortexed for 30 seconds. QC
dilution samples were prepared by further diluting the QC pool by 2, 3, 5 and 11 fold with
acetonitrile. A blank sample, consisting of only lysing buffer and another blank sample of which
the lysing buffer was substituted with water were also processed with the samples. All samples,
including blank and QC samples were transferred into 2 mL glass vials containing 250 µL glass
inserts with polymer feets (Agilent Technologies, Waldbronn, Germany) and were covered with
pre-slit polytetrafluoroethylene (PTFE)/silicone screw caps (Agilent Technologies, Waldbronn,
Germany).
LC-MS analysis
Samples were analyzed by using a 1290 Infinity UHPLC System coupled to a 6550 iFunnel
QTOF-MS from Agilent Technologies equipped with a Dual Agilent Jet Stream electrospray
source. In brief, each sample was analyzed separately on hydrophilic interaction liquid
chromatography (HILIC) (Acquity UPLC BEH Amide Column, 1.7 µm, 2.1 mm x 150 mm;
Waters, Eschborn, Germany) and on reversed phase liquid chromatography (RPLC) (Acquity
UPLC BEH, 1.7 µm, 2.1 mm x 100 mm; Waters, Eschborn, Germany) with temperature (auto
sampler and column), solvent and gradient settings as previously described 14. The injection
volume was set to 1.5-8 µL for HILIC and RPLC analysis, depending on the analysis batch or
mode. Between injections in RPLC and HILIC analysis, needle wash with 95 % acetonitrile was
performed. Mass spectrometric and auto MS/MS analysis settings were similar as previously
described 14, except, quadrupole band-pass for precursor isolation was set to narrow (∼1.3 m/z)
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for MS/MS acquisition. Cell, quadrupole, optics and funnel settings were adjusted separately for
lipid analysis on RPLC and small-molecule profiling on HILIC analysis according to
manufacturer’s instructions. In addition, the mass analyzer was calibrated on a daily basis
immediately before starting an analytical run. Moreover, in order to provide mass correction and
to assess ion suppression over the analytical gradient, two compounds (purine and HP-0921
[Hexakis(1H,1H,3H-tetrafluoropropoxy)phosphazene]) were added post-column via the
instrument internal calibrant delivery system. In positive ionization mode analysis the masses
m/z 121.0506 (purine) and m/z 922.0097 (HP-0921) and for negative mode analysis the masses
m/z 119.0363 (purine) and m/z 966.0007 (HP-0921) were used for dynamic mass correction.
Following concentrations in acetonitrile/water (95:5, v/v) were used for the two reference
masses: HILIC positive mode: 1.66 µM purine and 8.33 µM HP-0921; HILIC negative mode:
1.66 µM purine and 0.43 µM HP-0921; RPLC positive mode: 1.66 µM purine and 0.83 µM HP-
0921; RPLC negative mode: 0.56 µM purine and 0.83 µM HP-0921.
Metabolite annotation
For structural assignments of metabolomic features, mass spectra (MS1 and MS/MS) acquired
in QC samples were investigated with the Mass Hunter Qualitative Analysis Software (Version
B.06.00, Agilent Technologies). Features were annotated based on accurate mass (± 15 ppm)
and/or fragmentation patterns (MS/MS spectrum) that matched to data derived from online
databases LIPID MAPS 15, HMDB 16, METLIN 17 and MassBank 18 or literature 19,20.
Quantification of Metformin by LC-MS/MS
Tissue samples (20 – 30 mg) were homogenized in a total volume of 400 µl of 0.9% NaCl in a
FastPrep® 24 homogenizer (MP Biomedicals, Santa Ana, USA) for 20 s at speed 6.0 using
lysing matrix D. The homogenate was centrifuged for 10 min at 21130 x g. An aliquot of the
supernatant was spiked with internal standard [2H6]metformin and deproteinized with a threefold
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volume of 0.1 % formic acid in acetonitrile. After an additional centrifugation step, metformin was
quantified in the supernatant by LC-MS-MS using an Agilent 6460 triple quadrupole mass
spectrometer (Agilent, Waldbronn, Germany) coupled to an Agilent 1200 HPLC system.
Ionization mode was electrospray (ESI), polarity positive. Electrospray jetstream conditions were
as follows: capillary voltage 3500 V, nozzle voltage 1000 V, drying gas flow 11 l/min nitrogen,
drying gas temperature 300°C, nebulizer pressure 55 psi, sheath gas temperature 350 °C,
sheath gas flow 11 l/min. HPLC separation was achieved on a Synergi Polar-RP 80A column
(150×2 mm I.D., 4 µm particle size, Phenomenex, Aschaffenburg, Germany) using (A) 0.1 %
formic acid in water and (B) 0.1 % formic acid in acetonitrile as mobile phases at a flow rate of
0.4 ml/min. Gradient runs were programmed as follows: 10 % B from 0 min to 2 min, linear
increase to 50 % B to 5 min, then re-equilibration. The mass spectrometer was operated in the
multiple reaction monitoring (MRM) mode using m/z 130.1 and 136.1 as precursor ions for
metformin and [2H6]metformin, respectively, and the product ion m/z 60.1 for both compounds.
Dwell time was 100 ms, fragmentor voltage was set at 60, and the collision energy at 10.
Calibration samples were prepared by adding varying amounts of metformin to untreated tissue
homogenate. Concentration range was from 5 pmol to 5000 pmol per 25 µl of tissue
homogenate. Calibration samples were worked up as described above, and analyzed together
with the unknown samples. Calibration curves based on internal standard calibration were
obtained by weighted (1/x) linear regression for the peak-area ratio of the analyte to the internal
standard against the amount of the analyte. The concentration in unknown samples was
obtained from the regression line. Assay accuracy and precision were determined by analyzing
quality controls that were prepared like the calibration samples.
Isolation of RNA
Total RNA from animal and human kidney samples was isolated using the mirVanaTM miRNA
Isolation Kit (Ambion/Life Technologies) according to the manufacturer`s protocol. RNA quality
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and integrity was determined for all samples using the Agilent 2100 Bioanalyzer (Nano LabChip
Kit, Agilent Technologies). RNA Integrity Number (RIN) for all samples was above 7 and the
exact values for each sample are listed in Supplemental Table S-1.
Analysis of gene expression
Transcriptome profiling of kidney tissue samples of three control and three metformin treated
mice was performed using the Clariom S Assay (Affymetrix) according to the manufacturer`s
standard procedure (Affymetrix).
Data processing and statistical analysis
Preprocessing of nontargeted metabolomics data was carried out by using Mass Hunter
Profinder Software (version B.06.00 Agilent Technologies) using batch recursive and/or targeted
feature extraction with an intensity threshold ≥ 500. Settings were similar as described
previously 14. Extracted features were exported as comma separated value files and were used
for further statistical analysis. For reproducibility assessment (batch A), extracted features were
normalized using locally weighted scatterplot smoothing (LOESS) over quality control samples
(QCs) and the technical replicates 21. Coefficients of variation were calculated on the normalized
values. For each of the other batches (batch B1/B2 and C), the nontargeted and targeted feature
lists were sum normalized (peak area for each feature was divided by the sum of all features per
mode) and feature lists from the different analysis modes (HILIC and RPLC in positive and
negative ion mode) were merged. Of note, for batch C, the sum normalized values were
multiplied with 1x106. Subsequently, QC filtering (features with CV ≥ 30% over the QC samples
were removed) and log2 transformation was applied. Only features detected across all samples
were considered in all further analyses.
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Principal component analysis (PCA) was used to cluster ccRCC, oncocytoma and benign kidney
tissue of UCC (batch B1/B2). Pearson correlation coefficients (rp) were computed between log2
fold changes obtained by lysis buffer and standard protocol (batch B1/B2).
Quality control and preprocessing of microarrays was performed with Affymetrix Expression
Console (Build 1.4.1.46; annotation: Affymetrix ClariomS mouse Transcript Cluster Annotation,
Release 36). To be more precise, robust multi-array average 22 was applied to preprocess the
six Clariom S mouse arrays (processed data, Supplemental Table S-6).
For analysis of the exploratory proof of concept experiment (batch C) differentially altered
features (metabolites and transcripts) between metformin treated mice and control mice were
assessed by linear modeling and related empirical Bayes moderated t-tests 23. P-values were
corrected for multiple testing by the Benjamini-Hochberg procedure 24. For the assignment of
pathways to transcripts and metabolites the pathways glycolysis and gluconeogenesis,
triacylglyceride synthesis, TCA cycle and amino acid metabolism (for urea cycle) for mus
musculus were selected from WikiPathways 25,26. Pathways were chosen on the basis of known
modes of metformin action (i.e. lowering blood glucose level by affecting gluconeogenesis 27,28).
The GenMAPP Pathway Markup Language (gpml) files from WikiPathways were modified and
adapted using PathVisio (version 3.2.4) 29,30.
R statistics software (version 3.2.3) 31 was used for all statistical analyses using the additional
packages limma 32, beanplot 33, org.Mm.eg.db 34, data.table 35 and plyr 36. Radial plots were
generated in R with the radial.plot function within the plotrix package 37.
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RESULTS AND DISCUSSION
Combined transcriptomic and metabolomic analysis of tissue samples offers great potential to
study localized disorders and coupled signaling systems. However, tissue heterogeneity may
affect the concordance by superposition of small effects. Thus, the development of simultaneous
extraction procedures for the recovery of transcripts and metabolites from a single tissue
specimen represents a desired attempt. Although several protocols for the co-extraction of
metabolites/RNA and proteins from plants, cells and microorganisms have been described
previously, 38–41 these methods were not specifically tailored for human tissue analysis. One of
the most applied protocols for RNA isolation 11 was modified over the last couple of years and
was extended for the simultaneous extractions of RNA, DNA and proteins 13,42 but to our
knowledge not for metabolites. This motivated us to investigate the applicability of using
commercial lysing buffer for combined RNA isolation and metabolite extraction from kidney
tissue samples. For the protocol described herein tissue samples were homogenized, using
lysing buffer that is usually applied for RNA isolation, and subsequently divided for separate
RNA purification and metabolomics analyses. RNA quality is not compromised indicated by
average RIN values above 7 (Table S-1). For metabolomics analysis, homogenized tissue in the
lysis buffer was further diluted with acetonitrile followed by centrifugation and LC-MS analysis of
the supernatant. For method validation, ion suppression and assay reproducibility was
assessed.
Assessment of ion suppression and reproducibility
The lysing buffer contains several constituents (reducing agent, salts and detergent) and such
components may induce matrix effects. Here we investigated the effect of ion suppression for
two compounds with different molecular weights which were constantly infused post-column over
the whole gradient. As expected ion suppression was observed in all analytical modes however
with varying degrees (Supplemental Figure S-1 A-H). Whereas matrix effects in HILIC mode
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were generally more variable (Supplemental Figure S-1 A-D) signal reduction in RPLC mode
was more evenly distributed over the gradient (Supplemental Figure S-1 E-H). Of note, in HILIC
(-) mode ion enhancement was observed for the 966.0007 ion (Supplemental Figure S-1 D) in
most of the retention time regions indicating a beneficial effect with respect to detection
sensitivity for this compound.
Next, we investigated if reproducibility of both sample preparation and analytical measurement
was compromised on the basis of metabolic features measured in porcine kidney. Altogether,
2885 non-unique features, detected in different analytical modes, were considered (Table 1).
Table 1: Reproducibility assessment based on metabolomic features
Analysis mode
Total number of featuresa
Analytical reproducibility: number of features with CV ≤ 20%b
Sample preparation reproducibility: number of features with CV≤ 20%c
HILIC POS 265 263 (99%) 247 (93%) HILIC NEG 444 440 (99%) 422 (95%) RPLC POS 1087 1026 (94%) 902 (83%) RPLC NEG 1089 1022 (94%) 887 (81%) a Non-unique features. Features were not assessed for redundancy between modes (i.e. detectable as different adduct within or
between analysis modes).
b Coefficient of variation (CV) calculated over 8 QC samples analyzed throughout the analytical batch
c CV calculated over 10 individually prepared and analyzed kidney tissue samples
Of these, more than 93% and 80% showed excellent analytical and sample preparation
reproducibility (CV ≤ 20%), highlighting reliable extraction and analytical performance for the
majority of features (Table 1). Median CVs in positive and negative mode analysis for analytical
(HILIC: 5.2% / 4.7%; RPLC: 6.8% / 6.9%) and sample preparation reproducibility (HILIC: 7.4% /
6.9%; RPLC: 12.6% / 12.9%) were all below 15% indicating excellent assay precision (Figure 1).
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Figure 1
Similar proportions of reproducible features could also be found in previous metabolomics
studies, using conventional extraction and homogenization solvents composed of mixtures of
organic solvents and water 43–45. Of note, whereas analytical reproducibility of both HILIC and
RPLC analysis exhibited comparable CVs (median CVs ≤ 6% and ≤ 7%, respectively) sample
preparation reproducibility assessed by RPLC (median CV ≤ 13%) was higher compared to
those monitored by HILIC mode (median CV ≤ 8%). To assess which metabolite classes mainly
contribute to the observed differences, reproducibility was further examined on the basis of
structurally assigned features. To this end, CV values of 177 annotated features, covering
metabolite classes of various polarities, were visualized in radial plots (Figure 2). In these plots,
CVs for the indicated metabolite species and analytical modes are graphically represented as
distances from the center with circles representing different CV ranges. To enable a quick
visualization and differentiation between analytical and sample preparation reproducibility radials
were colored in blue and green, respectively. Information about CV values used to construct the
plots are provided in Supplemental Tables S2-S5
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As becomes apparent from the radial plots, the higher imprecision observed in the RPLC mode
(Figure 2 C/D and Supplemental Table S-4/S-5) can be attributed to lipids that exhibit larger CVs
compared to the more polar fraction of analytes captured by HILIC MS (Figure 2 A/B and Table
S-2/S-3). Such a higher imprecision in the RPLC mode is likely indicative of a less reproducible
extraction of features with intermediate and low polarity by the rather polar extraction solvent
used for RNA isolation. Of note, no particular lipid class seems to disproportionally contribute to
higher CVs as indicated by evenly distributed CVs over the individual species. Surprisingly, also
very non-polar lipids such as triacylglycerols (TAG) could be extracted and analyzed by RPLC
with CVs <20% (Figure 2 C) despite the usage of polar lysis solvent. We hypothesized that the
presence of detergents in the lysing buffer might support the solubility of lipids.
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Figure 2
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Method comparison with standard metabolomics protocol
The protocol for simultaneous RNA and metabolite extraction was compared to a standard
metabolomics workflow utilizing methanol/water for homogenization of an independent set of
tissue samples 14. First, using unsupervised statistical analysis, the ability to separate tumor
(ccRCC, oncocytoma) and non-tumorous (benign kidney tissue of UCC) kidney samples based
on all detectable features was assessed. Second, comparison was carried out on the basis of
annotated features to test whether similar biological effects (i.e. fold-changes) are produced by
the different approaches.
As demonstrated in Figure 3, technical replicates of tumor and non-tumorous samples could be
differentiated by PCA with comparable percentages of variance explained by 34%/35% (PC1)
and 26%/27% (PC2) for lysis buffer and standard protocol, respectively. In particular, ccRCC
and benign kidney tissue replicates could be separated on principle component 2 (PC 2)
whereas oncocytoma versus ccRCC and oncocytoma versus benign kidney tissue could be
separated on PC 1. This observation was consistent for both protocols and indicates the
capability to discriminate different types of human kidney samples. Noticeable, variation of
replicate sample analysis was higher for two tissue types (ccRCC and benign kidney tissue) in
the lysing buffer protocol whereas oncocytoma samples cluster together in both protocols.
Provided the fact that the lysis buffer protocol exhibit excellent samples preparation
reproducibility with CVs<20% for more than 80% of detected features (see previous section) the
method itself is unlikely to contribute to the observed variabilities which might rather originate
from tissue heterogeneity 14. A further likely explanation, that tissue homogenization in lysis
buffer may actually result in improved resolution of the variability between tumors, remains to be
investigated.
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Figure 3
The assessment of biological effects was examined on the basis of 102 annotated metabolites
which were previously shown to be detectable by a standard metabolomics approach 14.
Strikingly of these, 78 metabolites were detectable in all kidney tissue samples by the lysis buffer
protocol although with lower peak intensities compared to the standard protocol (Supplemental
Figure S-2 and S-3). Certain metabolites or metabolite classes such as specific acylcarnitines
(Figure S-2) were challenging to detect with the new approach. Still, besides a higher sample
dilution applied to the novel protocol, other factors might explain why these compounds could
not be detected as the protocols are inherently different in relation to parameters such as
homogenization solvents (RNA lysing buffer versus methanol/water and methyl tert-butyl
ether/methanol) or extraction procedures (e.g. one-step versus two-step). Limited stability may
be a conceivable explanation for the inability of the lysis buffer protocol to cover specific
compound classes. This assumption however is unlikely to explain about four times less features
(Figure 3) that rather result from a higher sample dilution or other parameters such as
differences in extraction yield, ionization efficiency or chromatographic performance in the
presence of residual salts. Thus, if the analysis of more focused molecular classes in envisaged,
sample derivatization46, changing of chromatographic conditions 43, implementation of filter-aided
sample preparation 47 or solid phase extraction procedures 48 may be considered. Nevertheless,
such techniques, which generally involve multiple steps, often result in higher variability and
protocol adaptions may thus occur at the expense of assay performance.
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Irrespective of these differences, the fold change profiles between the different samples
uncovered remarkably similar patterns of the two methods (Figure 4 A-C) with correlation
coefficients (Pearson) of rp ≥ 0.75 for all tissue entities (Figure 4 D-F), underlying the high level
of agreement between the two procedures. In addition, the high concordance between the
methods also suggests a comparable stability (i.e. short-term stability during sample processing)
of annotated metabolites in lysis buffer compared to the standard protocol. Further experiments
are required to assess a putative protective or even destructive effect of saturating salt
conditions on specific metabolomic and lipidomic components in tissue samples. Moreover,
since for transcriptomics investigations samples are often provided in solutions that maintain
RNA integrity (i.e. RNAlater), future experiments will have to investigate the feasibility to carry
out metabolomics profiling in these kind of preserved biobank samples.
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Figure 4
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Method application: monitoring of metformin-induced metabolomic and transcriptomic
alterations of kidney tissue
Transcripts and metabolites were analyzed in the kidney cortex of metformin treated healthy
mice (n=3) and a control group (n=3) (Supplemental Table S-1) to demonstrate method
applicability. The concentration of metformin in the kidney was quantitatively determined and
was approximately 1 µmol/g tissue (Supplemental Table S-1). In total, 372 structurally assigned
features (metabolites) and 22412 transcripts (Table S-6) were used for differential analysis of
which none were significantly altered between the two groups after adjustment for multiple
testing (Table S-7 and S-8). Yet, due to the limited sample size (n=3 per group) and hence low
statistical power this result was not unexpected. Moreover, it has to be kept in mind that mice
received only single dosing and the kidney was resected 1.5 h after drug administration. As
previously shown for human-derived colon cancer cells, metformin induces metabolic and
transcriptional changes in a time-dependent fashion 3. Thus, treatment status and time of tissue
sampling likely have an impact on the magnitude of change also in mice. It is of importance to
mention that the aim of this experiment was to demonstrate method applicability and further in-
depth investigation of metformin-induced effects would require analysis in larger sample cohorts
under steady state treatment.
As a consequence we focused on the assessment of drug-induced changes on transcript and
metabolite level within pathways based on known modes of metformin action (glycolysis and
gluconeogenesis, triacylglyceride synthesis, TCA cycle and amino acid metabolism). For
visualization (Figure 5), we selected transcripts (n=15) as well as metabolites (n=17) with
unadjusted p-value < 0.1 in our statistical analysis between metformin treated and control mice
that could be assigned to the pathways (Table S-7 and S-8). Within these, transcripts of
glycolytic enzymes (Hk1, Pfkm, Pfkp and Pkm) were upregulated, whereas G6pc, encoding a
key phosphatase in gluconeogenesis, was downregulated. In accordance with this observation
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reduced level of hexose was observed (Figure 5A) hence indicating an inhibitory effect of
metformin on the renal gluconeogenic glucose production. Moreover, Pdha2 and Pdk1,
transcripts involved in the transformation of pyruvate to Acetyl-CoA were increased upon
treatment (Figure 5B). Such an increased expression of the pyruvate dehydrogenase 2 (Pdha2)
is in accordance with the currently accepted gluconeogenesis suppression mechanisms of
metformin via increasing the lactate/pyruvate ratio 49. Likewise, a moderate reduction of the TCA
cycle intermediate-derived amino acids glutamine and glutamate (Supplemental Figure S-4)
indicates altered TCA cycle activity such as recently observed in primary rat astrocytes 50. Amino
acids of the urea cycle aspartate, citrulline and arginine revealed decreased levels accompanied
by reduction of guanidinioacetate a precursor of creatine synthesis. The transcript Arg2,
encoding arginase in the urea cycle, was increased pointing to the assumption that arginine
depletion may directly result from higher expression level of the corresponding hydrolase. In
accordance with the known beneficial effects on triglyceride production, a moderate reduction of
unsaturated TAGs was observed (Figure 5A), although no alterations of free fatty acid levels nor
of corresponding transcripts could be found (data not shown).
Taken together, the observed moderate changes of metabolite and transcript level derived from
this proof-of-concept experiment indicate that the established protocol is feasible and promising
in better understanding mode of drug actions as shown by the example of metformin. It is of
importance to note that observations from this experiment are preliminary and that the small
sample size limits the scope of biological interpretation. Further investigations, carried out in
larger cohorts, will be required to confirm findings.
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Figure 5
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CONCLUSION
In this manuscript, the reproducibility, comparability and the applicability of a novel protocol for
combined metabolomics/transcriptomics analysis in kidney tissue could be demonstrated. The
procedure of bead-based tissue homogenization in RNA lysis buffer has been proven to allow
efficient and reproducible metabolite extraction without affecting RNA quality. Importantly, the
applicability of bead-based metabolite extraction procedures in metabolomics 44,45 and the use of
lysis buffers for RNA isolation 51 have been demonstrated for other tissue types than kidney (e.g.
liver). Hence the protocol presented here provides a solid basis for being readily transferred to
other tissues which in turn may support the discovery of novel tissue-specific gene expression
signatures and related metabolites.
The major advantages of the presented method are the simultaneous extraction of RNA and
metabolites from a single tissue specimen in a fast and simple manner, without harsh
modifications of the standard workflow for RNA isolation. Moreover as previously shown, a
protocol using similar lysing buffers enabled also the recovery of proteins and DNA from tissue
samples 13, offering the potential to expand the current protocol to other omics disciplines in the
future. In this regard, the adaption and implementation of innovative sample preparation
strategies such as SIMPLEX 52 should be considered in particular to capture proteomics
information as well. Implementation of such information into the proposed workflow will be of
major interest to better understand the functional consequences of genetic variation across
human tissues 53 at both transcript (eQTL) and proteomic (pQTL) level. Notably, having clinical
information available in addition to multi-omics data will support a more systematic exploration of
associations between clinical phenotypes and molecular attributes. Finally, the current protocol
could be applied to improve identification and functional characterization of tumor heterogeneity
which in turn may support a more refined investigation of tumor evolution.
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ASSOCIATED CONTENT
Supporting Information
Table S-1: RNA Integrity Numbers (RIN) for the different analyzed samples and kidney tissue
metformin concentrations of the mouse samples. Table S-2: Annotated metabolites to assess
reproducibility in HILIC-ESI-(+) mode. Table S-3: Annotated metabolites to assess reproducibility
in HILIC-ESI-(-) mode. Table S-4: Annotated metabolites to assess reproducibility in RPLC-ESI-
(+) mode. Table S-5: Annotated metabolites to assess reproducibility in RPLC-ESI-(-) mode.
Table S-6: Microarray data. Table S-7: Statistical analysis of transcriptomics data between
metformin treated and control mice including pathway information. Table S-8: Statistical analysis
of metabolomics data between metformin treated and control mice including pathway
information. Table S-9: List of abbreviations for lipids. Figure S-1: Assessment of ion
suppression. Figure S-2: Barplots representing mean peak heights for the lysis buffer and the
standard protocol. Figure S-3: Beanplots representing the mean peak heights of 102
metabolites. Figure S-4: Intermediates involved in the TCA-cycle.
AUTHOR INFORMATION
Corresponding Author
E-Mail: [email protected]. Phone +49 (0)711 / 8101-5429
Author Contributions
P.L. and M.H. performed the experiments for the non-targeted metabolomics analysis, mainly
contributed to the conception and design of the manuscript, performed the statistical analysis
and drafted the manuscript. M.S, E.S. and U.H contributed to the conception, design and
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revision of the manuscript. S.R., J.H. and J.B. revised the manuscript and were involved in the
process of sample collection (human kidney tissue). S.W. was involved in the revision of the
statistical analysis and the manuscript. M.P. was responsible for the metformin treatment
experiments in mice and provided mice kidney tissue samples. All authors have given approval
to the final version of the manuscript. All other authors declare no competing financial interest.
ACKNOWLEDGEMENTS
The work was supported by the Robert Bosch Stiftung and the Bosch-Forschungsstiftung as well
as the ICEPHA Graduate School Tübingen-Stuttgart. Ursula Waldherr, Monika Elbl and Markus
König are gratefully acknowledged for excellent technical assistance.
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Figures
Figure 1 Assessment of analytical and sample preparation reproducibility based on metabolomic
features. Beanplots show the distribution of CVs for analytical (blue) and sample preparation
(green) reproducibility for HILIC and RPLC analysis in positive and negative ionization mode,
respectively (Batch A). Median CVs based on metabolomic features are indicated by the larger
horizontal black lines and exact values are listed above the beanplots. Black thin lines are
representing the CV of each feature analyzed in the respective modes. Data processing was
carried out by untargeted feature extraction.
Figure 2 Assessment of analytical and sample preparation reproducibility of annotated
metabolites. Radial plots representing the distribution of the CVs of each annotated feature for
analytical (blue) and sample preparation (green) reproducibility for HILIC (A-B) and RPLC (C-D)
analysis in positive and negative electrospray ionization (ESI) mode, respectively (Batch A).
Features were annotated based on accurate mass and fragmentation patterns (MS/MS
spectrum) that matched to data derived from online databases or literature. The numbers in
each radial plot are referring to the CVs in percent. Plots were generated on the basis of mean
CV values listed in Tables S2-S5 (Supporting Information). Data processing was carried out by
targeted feature extraction.
Figure 3 Comparison of the metabolite profile analyzed with the new protocol to an established
metabolomics approach. A: PCA of Batch B1 (n=3 technical replicates of ccRCC, onocytoma
and benign UCC kidney tissue, respectively) applying the new protocol with commercial lysis
buffer for tissue homogenization (4549 features). B: PCA of Batch B2, (n=6 technical replicates
of ccRCC, onocytoma and benign kidney tissue from UCC, respectively) using an established
metabolomics approach (19026 features) with a two-step extraction procedure 14. Note: ccRCC,
oncocytoma, and benign tissue are derived from 3 different donors. From each donor tissue 9
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different pieces were cutted and 3 pieces were chosen for the lysis buffer and 6 for the standard
protocol. Samples are thus matched by patient/tissue.
Figure 4 Method comparison of the novel protocol (Batch B1) using commercial lysing buffer
(orange) to standard protocol (Batch B2) using methanol/water for tissue homogenization (blue)
14. Log2 fold changes were calculated for each assigned metabolite found in all samples with
both protocols (n=78) between the clear cell renal cell carcinoma (ccRCC), oncocytoma (OC)
and benign UCC kidney tissue (A-C). Pearson correlation coefficients (rp) for the log2 fold
changes between the different kidney tissue entities (D-F).
Figure 5 Transcripts and metabolites involved in glycolytic and related pathways (Batch C).
Differentially altered transcripts/metabolites (Table S-7 and Table S-8) were assessed by linear
modeling and related empirical Bayes moderated t-tests 23. Transcripts and metabolites with
unadjusted p-values <0.1 that could be assigned to the indicated pathways were selected for
visualization. Barplots representing mean metabolite (A) and transcript levels (B) between
control (n=3) and metformin treated mice (n=3). The error bars represent the standard deviation.
Fold-changes (Log2FC) indicate magnitude of increase and decrease, respectively. Transcripts
and metabolites from the barplots are depicted in the respective pathways (C). Sharp and round
corners of boxes indicate method of measurement. Note: metabolites in black frames (i.e. sugar
phosphates) were not detected by the metabolomics assay.
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For TOC only
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