This document summarizes Adriana Zardini Buzatto's background and research experience in lipidomics and metabolomics. It outlines her educational background, including undergraduate and graduate degrees from universities in Brazil and Canada. It also provides an overview of several of her current and past research projects applying lipidomics techniques to study conditions like cystic fibrosis, spinal cord injury, and Parkinson's disease. These projects involve analyzing small biological samples using nanoLC-MS for lipid profiling to better understand disease mechanisms and identify potential biomarkers.

1. LIPIDOMICS AND METABOLOMICS OF
SMALL AMOUNTS OF BIOLOGICAL
SAMPLES
Adriana Zardini Buzatto
R&D Senior Scientist
Nova Medical Testing, Inc.
The Metabolomics Innovation Centre (TMIC)
University of Alberta
zardinib@ualberta.ca

2. OUTLINE
Untargeted lipidomics and metabolomics
PhD (Doctoral Dissertation Award)
UNIVERSITY OF ALBERTA (CANADA)
Untargeted lipidomics
FUTURE RESEARCH
Untargeted lipidomics
R&D Senior Scientist
THE METABOLOMICS INNOVATION CENTRE
NOVA MEDICAL TESTING
Targeted metabolomics (nucleosides)
Undergraduate research and Master’s degree
UNIVERSITY OF CAMPINAS (BRAZIL)
01
02
03
04
04

3. Calibration set Validation set
Accuracy (%) 70.6 82.4
Sensitivity (%) 71.4 90.5
Specificity (%) 70.0 76.7
MASTER’SDEGREE:TARGETEDMETABOLOMICS
ANALYSIS OF NUCLEOSIDES, PUTATIVE TUMOR BIOMARKERS FOR PROSTATE CANCER, BY CE-UV
PSA: Sensitivity of 20 – 25% and specificity of 90% (0 to 4.0 ng/mL)
60 HEALTHY VOLUNTEERS VS. 42 PROSTATE CANCER PATIENTS
C A U 5mU G X I
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
Variable
Importance
(VIP)
Dr. Ana V. C. Simionato
University of Campinas, Brazil
Dr. Ronei Poppi
University of Campinas, Brazil
Inosine
(I)
5-Methyluridine
(5mU)
Uridine
(U)
Xanthosine
(X)
8-Bromoguanosine
(8BrG)
Guanosine
(G)
Thymidine
(T)
Cytidine
(C)
Adenosine
(A)
2’-Deoxyadenosine
(2dA)

4. DOCTORATE:UNTARGETEDLIPIDOMICS
LIPIDS: HYDROPHOBIC OR AMPHIPHILIC METABOLITES WITH LOW SOLUBILITY IN WATER AND HIGH SOLUBILITY IN NON-
POLAR SOLVENTS
POLYKETIDES
PRENOL LIPIDS
SACCHAROLIPIDS
STEROL LIPIDS
GLYCEROPHOSPHOLIPIDS
SPHINGOLIPIDS
GLYCEROLIPIDS
FATTY ACIDS
Dr. Liang Li
University of Alberta, Canada

5. LIPIDS:BIOLOGICALFUNCTIONS
DIVERSITY OF FUNCTIONS, PATHWAYS AND BIOLOGICAL PROCESSES
Membranes
Immune response
Signaling
Modulation
Energy
Recognition of
pathogens; activation of
immune pathways
Protein folding, transcription, transport
Adipose tissue
Intra- and inter-cell
communication
Structure and
compartmentalization

6. IDEALLIPIDOMICSWORKFLOW
FROM SAMPLE COLLECTION TO BIOLOGICAL IMPLICATIONS
Sample collection and
storage, extraction of
lipids, clean-up, dilution
SAMPLE PREPARATION
Alignment, mass
correction, isotopes and
adducts, identification
DATA PROCESSING
Pathway analysis,
biological processes,
metabolic reactions
BIOCHEMISTRY
Targeted versus
untargeted approaches;
shotgun lipidomics;
LC-MS, GC-MS, NMR
ANALYSIS
Normalization, statistical
models, selection of
important lipids
STATISTICS
1 2 3 4 5

7. NanoLC-MSWORKFLOWFORGLOBALLIPIDOMICANALYSIS
MITACS ACCELERATE GRANT (COLLABORATION WITH THE RICK HANSEN INSTITUTE)
ZARDINI BUZATTO, A., KWON, B. LI, L., ANALYTICA CHIMICA ACTA 2020, 1139, 88-99
12260
9902
2845
2915
3505
4027
4895
4714
0
4000
8000
12000
16000
25X 10X 5X 2.5X 1.25X 1X
Number
of
detected
features
Dilution of the serum extract
NanoLC-MS versus UHPLC-MS: serum
nanoLC:
2.5 µL
UHPLC:
25.0 µL
NANOLC: HIGH SENSITIVITY, LOWER ROBUSTNESS

8. LIPIDOMICSOFSPINALCORDINJURYUSINGNANOLC-MS
PILOT STUDY: YUCATAN MINI-PIGS AS ANIMAL MODELS
MITACS ACCELERATE GRANT (COLLABORATION WITH THE RICK HANSEN INSTITUTE) • IMAGE ADAPTED FROM OKON, E. et al., JOURNAL OF NEUROTRAUMA 2013, 30(18), 1564-1576
ZARDINI BUZATTO, A., KWON, B. LI, L., ANALYTICA CHIMICA ACTA 2020, 1139, 88-99
0
500
1000
1500
2000
2500
Identified
lipids
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
Identified
lipids
(%)
Cerebrospinal fluid: 2.5 µL
Serum: 2.5 µL
Parenchymal microdialysate: 1.0 µL
Dr. Brian Kwon
University of British Columbia, Canada

9. LIPIDOMICSOFSPINALCORDINJURY
TIME SERIES FOR PARENCHYMAL MIDRODIALYSATE (1.0 µl / SAMPLE)
CRITERIA FOR SIGNIFICANCE: FOLD-CHANGE (FC) ≥1.5 OR FC ≤0.67 AND P-VALUE ADJUSTED FOR FALSE-DISCOVERY RATE (FDR-P) <0.05
UNPUBLISHED DATA
0
10
20
30
40
4.75h / 6.75h 4.75h / 8.75h 4.75h / 10.75h
0
10
20
30
40
Acer
AcylGlcADG
BMP
Car
CE
Cer
CL
CoA
DG
DGT
FA
FC
HexCer
LPA
LPC
LPE
LPG
LPI
LPS
MG
MIPC
NAA
Other
PA
PC
PE
PE-Cer
PEtOH
PG
PI
PIP
PMeOH
PPKT
PS
SM
SPB
ST
Sulf
TG
CRITERIA FOR SIGNIFICANCE: fold-change (FC) ≤0.67 or ≥1.5 and
p-value adjusted for false-discovery rate (FDR-p) <0.05
Number
of
significantly
altered
lipids
FDR-p<0.05, fold-change ≥1.5 (higher intensities for 4.75 h)
FDR-p<0.05, fold-change ≤0.67 (lower intensities for 4.75 h)
-100 -50 0 50 100 150
PC1 (21.0%)
PC2
(15.0%)
-50
0
50
● 4.75h
● 6.75h
● 8.75h
● 10.75h

10. LIPIDOMICSANDMETABOLOMICSOFLUNGTISSUEFORTHESTUDYOFANOVEL
VACCINEAGAINSTRESPIRATORYSYNCYTIALVIRUS(RSV)
INVESTIGATION OF MECHANISMS OF ACTION USING MICE AS AN ANIMAL MODEL
Group B
Immunized;
RSV challenged
Group C
Non-immunized;
RSV challenged
Group A
Control
Lipidomics
Metabolomics
(amine and phenol )
Metabolomics
(carboxylic acid)
Dr. Sylvia van Drunen Littel-van den Hurk
University of Saskatchewan, Canada

11. Surfactant
RSVANDTHELUNGSURFACTANTLAYER
LUNGS COLLECTED SEVEN DAYS AFTER RSV CHALLENGE
PC 16:0/16:0 PC 30:0 PC 32:1
■ Control ■ RSV, immunized ■ RSV, non-immunized
Similar behavior for POPG (PG 16:0_18:1) and POPC (PC 16:0_18:1)

12. LIPIDOMICSOFPARKINSON’SDISEASEANDDEMENTIA
5 lipids
Area under the curve (AUC) = 0.973
95% Confidence Interval= 0.919 - 1.000
0.0 0.2 0.4 0.6 0.8 1.0
0.0
1.0
0.2
0.4
0.6
0.8
1 – Specificity (false positive rate)
Sensitivity
(true
positive
rate)
30
15
0
-15
Component
2
(10.3%)
1 - Dementia
2 - No dementia
Sensitivity: 100%
Specificity: 100%
Accuracy: 100%
R2 = 0.970 • Q2 = 0.764
p <0.04 for 1000 permutations
Dr. Richard Camicioli
University of Alberta, Canada
Dr. Roger Dixon
University of Alberta, Canada
Prediction of dementia 3 years before clinical diagnosis using serum samples

13. LIPIDOMICALTERATIONSINDUCEDBYCYSTICFIBROSIS
UNTERGETED LIPIDOMICS OF SERUM
Intestinal blockage, poor
nutrient absorption
DIGESTION
LUNGS
Infections, coughing, inflammation,
permanent damage
PANCREAS
Pancreatic insufficiency
REPRODUCTION
Infertility, complications
during pregnancy
Life expectancy
Canada
50.9
years old
US
40.6
years old
Poor
countries
>15
years old
• Mutations in the CFTR gene
• Traffic of chloride ions and water through membranes
• Accumulation of thick, desiccated mucus
Dr. Anas M Abdel Rahman
King Faisal Specialist Hospital and
Research, Saudi Arabia
Dr. Majed Dasouki, MD
King Faisal Specialist Hospital and
Research, Saudi Arabia

14. CYSTICFIBROSIS:DATAQUALITY
0
200
400
600
800
1000
1200
Acer
AcylGlcADG
BMP
Car
CE
Cer
CL
CoA
DG
DGT
FA
FC
HexCer
LPA
LPC
LPE
LPG
LPI
LPS
MG
MIPC
NAA
Other
PA
PC
PE
PE-Cer
PEtOH
PG
PI
PIP
PMeOH
PPKT
PS
SM
SPB
ST
Sulf
TG
Identified
lipids
Tier 3 Tiers 1 and 2
Tier 1
879
Tier 2
467
Tier 3
5600
Tier 1 m/z error ≤ 5.0 mDa, MS/MS score ≥500, mSigma ≤150
Tier 2 m/z error ≤ 5.0 mDa, MS/MS score ≥100, mSigma ≤50
Tier 3 m/z error ≤ 5.0 mDa Quality control: 42
extraction replicates
CYSTIC FIBROSIS
CONTROL

15. CYSTICFIBROSIS(CF)DIAGNOSIS
P784.58482/8.5
PC 15:1_21:2
FC 35.4
p = 3.11×10-26
AUC = 0.996
95% CI: 0.950 – 1.00
0.0 0.2 0.4 0.6 0.8 1.0
1-Specificity (false positive rate)
Sensitivity
(true
positive
rate)
0.0
0.2
0.4
0.6
0.8
1.0
CF Control
P784.584841/9.16
PC 15:1_21:2
FC 29.5
p = 3.11×10-26
AUC = 0.996
95% CI: 0.950 – 1.00
0.0 0.2 0.4 0.6 0.8 1.0
1-Specificity (false positive rate)
Sensitivity
(true
positive
rate)
0.0
0.2
0.4
0.6
0.8
1.0
CF Control
20191125P_CF5_1_RA3_01_31152.d
20191125P_Ctrl17_2_GB3_01_30905.d
0.0
0.2
0.4
0.6
0.8
5
x10
Intens.
0.0
0.2
0.4
0.6
0.8
5
x10
Intens.
6.5 7.0 7.5 8.0 8.5 9.0 9.5 Time [min]
CF patient
Healthy control
PC 19:2_19:2
CF
Control
Normalized
intensities
PS 22:0_17:2
CF
Control
Normalized
intensities
Accuracy:
97.9%
Accuracy:
100%

16. CURRENTWORK
IN-DEPTH GLOBAL LIPIDOMICS PLATFORM (THE METABOLOMICS INNOVATION CENTRE AND NOVAMT)
ESI+ ESI-
LIQUID-LIQUID EXTRACTION OF LIPIDS
LC-ESI-QToF (MS/MS)
DATA PROCESSING: NOVAMT LIPIDSCREENER
BIOSTATISTICS: NOVAMT LIPIDSCREENER
Modified Folch method Custom mixture of deuterated lipids
Identification of thousands of lipids
Reliable, robust method
Custom software for handling big dataset Complete solution
Dr. Liang Li
University of Alberta, Canada
UNPUBLISHED DATA

17. LIPIDOMICSOFSMALLEXTRACELLULARVESICLES(EVs)OFCELLS
EXTREMELY DILUTED SAMPLES: HIGH SENSITIVITY ANALYSES
lysosomal
degradation
nucleus
early
endosome
ER
multivesicular
body
microvesicles
100-1000 nm
Endosome
with intraluminal
vesicles
target cell
exosomes
30-100 nm
lipid
membrane
proteins
proteins
DNA
RNA
metabolites
lipids

18. LIPIDOMICSOFSMALLEXTRACELLULARVESICLES(EVs)OFCELLS
ARE RNA TRANSFECTION COMPLEXES (LIPOFECTAMINE RNAiMAX REAGENT) EXCRETED INSIDE EXOSOMES?
PLS-DA
EVs from
transfected cells
EVs from non-
transfected cells
Transfection
reagent
R2: 0.9982
Q2: 0.9975
p < 0.001
(1000 perm.)
target
cell
transfected
cell
RNAiMAX?
0
50
100
150
200
250
300
350
ACer
BMP
Car
CDP-DG
CE
Cer
CerP
CL
CoA
DG
DGCC
DGDG,…
DGMG,…
DGTA,
DGTS
FA
FAG
FAHFA
FAL
FOH
GlcADG
GlcCer
Glc-GP
GP
HC
HexCer
HexSPB
LPA
LPC
LPE
LPG
LPI,
LPIM
LPS
LSM
MG
MIPC,…
NA
NAE
NAPE
NAT
Other
PA
PC
PE
PE-Cer
PE-Nme,…
PG
PGP
PGS
PI
PI-Cer
PIM
PIP
PK
PnC,
PnE
PPA
PR
PS
PS-NAc
PT
SCer
SHexCer
SL
SLBPA
SM
SPB
SPBP
SQDG
SQMG
ST
SulfateHex…
TG
WE
Number
of
identifications
Lipid subclass
Out of 5477 detected features, 2743 features (50.1%) were identified
Tier 1 Tier 2 Tier 3

19. LIPIDOMICSOFSMALLEXTRACELLULARVESICLES(EVs)OFCELLS
ARE RNA TRANSFECTION COMPLEXES (LIPOFECTAMINE RNAiMAX REAGENT) EXCRETED INSIDE EXOSOMES?
CRITERIA FOR SIGNIFICANCE: FOLD-CHANGE (FC) ≥1.4 OR FC ≤0.71 AND P-VALUE <0.05
376 65
86
256
48
8
1141
Transfected
Non-transfected
RNAiMAX
p-value = 0.07
FC = 1.15
transfected
cell
RNAiMAX?

20. DIFFERENTTYPESOFSAMPLES
SELECTED PROJECTS COMPLETED WITH TMIC AND NOVAMT (2021/2022)
TISSUES
Lung, brain, liver, kidney, muscle,
lymph node, spinal cord, cecum,
placenta, artery
OTHERS
Urine, milk, lipid droplets, saliva, mosquito
guts (infection with dengue fever), fruit fly
Mammal cells (neurons, trophoblast cells,
seminiferous tubules, T cells), bacteria, yeast
Enzyme knockout models, exposure to
toxins, diseases, interventions
PLANTS
Canola leaf and roots,
cattle plant feed
SERUM/PLASMA
GROWTH MEDIUM
Parasites (worms),
pathogens, cancer cells
CELLS
Arthritis, dementia, Alzheimer’s disease,
multiple sclerosis, diabetes, Gulf War
illness, spinal cord injury, diet effects, ALS

21. PASTANDCURRENTWORK:SUMMARY
New methodologies for untargeted lipidomics of different types of biological samples
Improved sensitivity, robustness, quantification and identification of a diversity of
lipid species through optimization of data acquisition and processing routines
Biochemical evaluation leading to new findings for a variety of conditions
Different applications: biomarker discovery, characterization of cells, response to
external perturbations, diet effects
Integration with metabolomics for better characterization of physiological and
pathological processes

22. FUTUREPLANS
PROPOSED RESEARCH LINES
01
02
03
Development and validation of novel methodologies for lipidomics
of biological samples
Investigation of metabolic reactions in different cell lines
Biomarkers of human pathologies and physiological phenomena

23. DEVELOPMENTOFNOVELMETHODOLOGIESFORLIPIDOMICS
PLAN OVERVIEW
LC-MS method
Qualitative Quantitative
Identifications
Unknowns
Sensitivity
Low concentrations
Relative
Biomarker research
Absolute
Pathway analysis
Untargeted
Unknowns
Targeted
Lipid subclass
Reactions
tandem-MS
Libraries
Other
techniques
nanoLC, 2D-LC,
GC-MS,
shotgun, NMR
Functional groups
ROBUSTNESS, COSTS

24. DEVELOPMENTOFNOVELMETHODOLOGIESFORLIPIDOMICS
EXPECTED RESULTS
• Essential step to pursue further advances for untargeted lipidomics
• Quick and long-lasting results
• Opportunity of branching into multiple sub-lines
• Double bond location, stereoisomerism, enzymatic and chemical structural
modifications, high-sensitivity applications, clinical testing
• Possibility of collaborations with industry and academic institutions
• Acquisition of instruments and consumables in exchange for the development of
methodologies and resources for lipidomics
• Targeted method development for characterization of special types of lipids or samples

25. INVESTIGATIONOFMETABOLICREACTIONSINDIFFERENTCELLLINES
CHARACTERIZATION OF LIPIDS WITHIN CELLS, ORGANELLES, CULTURE MEDIA AND EXTRACELLULAR VESICLES
Investigation of the lipid composition
of different cell lines (mammalian,
bacteria, fungi, pathogens, stem cells)
and how it is affected by pathologies,
stressors, toxins, and metabolic
modifications.
Investigation of cell-to-cell signaling,
communication and interactions.
Characterization of alterations related
to diseases, external threats,
interventions, etc.
Intra-cell Inter-cells
Characterization
Response to
perturbations
CELL LIPIDOMICS
Culture media
Extracellular
vesicles
Biochemical
processes
Pathologies

26. INVESTIGATIONOFMETABOLICREACTIONSINDIFFERENTCELLLINES
EXPECTED RESULTS
• Screenshot of physiological or pathological reactions that affect homeostasis and
inter-cell relationship
• Cross-disciplinary bridge with other programs and facilities
• e.g., the Chemistry, the Biochemistry and the Cell, Microbial and Molecular Biology programs
• Calgary Metabolomics Research Facility, International Microbiome Centre
• Potential to generate an extensive number of interesting publications
• Possibility of collaboration with other research groups and the pharmaceutical/
medical industry
• Investigation of the effects of new interventions and medications
• Integration with metabolomics, proteomics, glycomics, transcriptomics, genomics

27. BIOMARKERSOFHUMANPATHOLOGIESANDPHYSIOLOGICALPHENOMENA
BIOMARKER DISCOVERY: HUGE POTENTIAL FOR PUBLICATIONS AND TRANSLATIONAL RESEARCH
BIOMARKERS
Diagnosis and
assessment
Prediction of
recovery
Disease
development
Animal model /
human translation

28. • More complex line requires further collaborations, particularly for sample collection
• Interaction with researchers in the health sciences, biochemistry and biology areas
• Cumming School of Medicine, Clark H. Smith Brain Tumor Centre - Tumor Bank, Health Sciences Animal Resource
Centre, Biorepositories (Alberta Health Services, the Alberta Children's Hospital Foundation)
• Cross-disciplinary bridge with the Chemistry, the Biochemistry, the Biological/Medicinal Chemistry, and
the Health Science programs
• Calgary Metabolomics Research Facility (CMRF), Alberta Centre for Advanced Diagnosis (ACAD)
• Possibility of collaboration with the pharmaceutical/medical industry
• Significant findings, high-impact publications, future expansion
• Long-term goal: translating research to benefit the population
• Biomarker discovery, characterization, validation, and translation
BIOMARKERSOFHUMANPATHOLOGIESANDPHYSIOLOGICALPHENOMENA
TRANSLATION FROM RESEARCH LAB TO CLINICAL APPLICATIONS

29. CONCLUSIONS
• Better methodologies
• Identifications: improved accuracy
• Absolute quantitation
• Biochemical pathways, metabolic
reactions
But so much to do!
• Small volumes of biological fluids
• Sensitive, reliable methods
• Different types of samples
• More identifications
So much has been done…