Jun 17, 2026
Cardiac Tissue-Specific Cell-Free DNA Methylation Profiling for Preclinical Detection of Hereditary ATTR Cardiomyopathy (hATTR-CM)
- 1Baylor Scott & White Research Institute
Protocol Citation: Hakeem Arzu 2026. Cardiac Tissue-Specific Cell-Free DNA Methylation Profiling for Preclinical Detection of Hereditary ATTR Cardiomyopathy (hATTR-CM). protocols.io https://dx.doi.org/10.17504/protocols.io.3byl4m7erlo5/v1
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
We use this protocol and it's working
Created: June 14, 2026
Last Modified: June 17, 2026
Protocol Integer ID: 319117
Keywords: transthyretin amyloidosis, hereditary ATTR cardiomyopathy, Val122Ile, cell-free DNA, cfDNA methylation, liquid biopsy, tissue-of-origin deconvolution, preclinical biomarker, CelFiE, Loyfer atlas, pharmacodynamic biomarker, health equity, amyloid cardiomyopathy, hereditary attr cardiomyopathy, molecular cardiomyocyte injury biomarker component17, specific cfdna methylation with asymptomatic hattr mutation carrier, endpoint with no molecular cardiomyocyte injury biomarker component17, first heart failure diagnosis, significant cardiomyocyte, hereditary transthyretin amyloid, patients with heart failure, cardiomyocyte, heart failure, free dna methylation profiling for preclinical detection, days from first heart failure diagnosis, specific cfdna methylation, free dna methylation profiling, asymptomatic ttr mutation carrier, specific methylation signatures into the bloodstream, cardiac cell, asymptomatic ttr mutation carriers as an area, cardiac tissue, asymptomatic hattr mutation carrier, cfdna methylation patte
Abstract
Background: Hereditary transthyretin amyloid cardiomyopathy (hATTR-CM) is a
progressive, infiltrative disease for which three disease-modifying therapies
are now approved — tafamidis,14 acoramidis,15 and
vutrisiran16 — but
whose efficacy is greatest when initiated before significant cardiomyocyte
loss. An estimated 10–15% of patients with heart failure with preserved
ejection fraction (HFpEF) have undiagnosed ATTR-CM.1 A large-scale Medicare cohort study found a
median diagnostic delay of 494 days from first heart failure diagnosis to
ATTR-CM diagnosis — a figure that has not improved from 2016 to 2022 despite
expanding awareness and available therapies.3 The American Heart Association has explicitly identified monitoring in
asymptomatic TTR mutation carriers as an area of significant uncertainty, with
no accepted definition of progression or response to therapy currently
established.5
Scientific
Rationale: When cardiomyocytes die from amyloid
infiltration, they shed cell-free DNA (cfDNA) fragments carrying
cardiac-specific methylation signatures into the bloodstream — even before
imaging changes appear. Lehmann-Werman et al. established that cardiomyocyte-specific
cfDNA methylation patterns are detectable in human plasma during cardiac cell
death, with markers validated across all cardiac chambers.10 We propose applying the CelFiE tissue-of-origin
deconvolution algorithm9 against
the Loyfer et al. 39-tissue methylation reference atlas (Nature, 2023)8 to quantify the cardiac-fraction cfDNA in
peripheral blood plasma. Critically, this methodology uses methylation-based
tissue-of-origin deconvolution rather than SNP-based approaches, making it
applicable in hATTR-CM where cardiac and blood cell cfDNA are genetically
identical.
Proposed Study: A Phase 1 prospective observational pilot (N = 125) enrolling five
cohorts across 24 months, with plasma collected at months 0, 6, 12, 18, and 24
using standardized pre-analytical protocols. Four specific aims address:
reference distribution establishment (Aim 1); subclinical injury detection in
asymptomatic hATTR carriers versus matched controls (Aim 2); landmark
progression analysis linking cfDNA trajectory to composite cardiac endpoint
(Aim 3); and treatment response monitoring on TTR stabilizer therapy as an
exploratory pharmacodynamic biomarker (Aim 4). Val122Ile TTR variant carriers
are a priority sub-population given their prevalence of approximately 3–4% in
African Americans6 and
profound underrepresentation in ATTR research.
Novelty: A systematic search across PubMed, ClinicalTrials.gov, Google Patents,
and major cardiovascular conference abstract archives (2018–2026) identified no
published studies, registered trials, or patents combining cardiac
tissue-specific cfDNA methylation with asymptomatic hATTR mutation carrier
surveillance. ACT-EARLY (NCT06563895), the first Phase 3 ATTR prevention trial
enrolling asymptomatic carriers, uses radionuclide imaging as its primary
endpoint with no molecular cardiomyocyte injury biomarker component17 — confirming the gap this proposal directly
addresses. The field has explicitly called for longitudinal studies integrating
cfNA methylation signatures to distinguish myocardial injury from systemic
tissue damage.24
Attachments
Materials
Blood Collection Supplies
•
Streck Cell-Free DNA BCT tubes (preferred) —
Streck, Inc.
•
Standard EDTA (K2EDTA) tubes — alternative if
processed within 2 hours
•
Standard phlebotomy supplies (needles, alcohol
swabs, tourniquet, gauze)
Plasma Processing Equipment
•
Refrigerated centrifuge capable of 1,600g and
16,000g
•
Cryovials for plasma aliquot storage
•
−80°C freezer for long-term plasma storage
•
Barcode/chain-of-custody labeling system
Sequencing & Library Preparation
•
Whole-genome bisulfite sequencing (WGBS) kit —
Zymo-Seq Trio Kit (Aim 1 atlas construction only)
•
Targeted bisulfite sequencing panel — Zymo
Research or Daicel Arbor Biosciences myBaits Custom Methyl-Seq (Aims 2–4)
•
Enzymatic methylation sequencing (EM-seq) kit —
New England Biolabs (recommended scale-up option)
Bioinformatics Infrastructure
•
CelFiE deconvolution algorithm (Caggiano et al.,
2021) — open-source, available via GitHub
•
Loyfer et al. 39-tissue methylation reference
atlas (Nature, 2023) — publicly available reference dataset
•
Computing infrastructure capable of batch
processing sequencing reads (institutional HPC cluster or cloud computing
recommended)
Clinical Assessment Tools
•
Echocardiography with global longitudinal strain
(GLS) capability
•
Cardiac MRI with extracellular volume (ECV) and
late gadolinium enhancement (LGE) protocols
•
Standard clinical laboratory panel:
eGFR/creatinine, NT-proBNP
•
ASCVD 10-year risk score calculator; coronary
artery calcium (CAC) scoring where feasible
Safety warnings
• Genotype-negative control recruitment: sourcing genetically-confirmed TTR-negative individuals matched to carriers requires either cascade family screening or pre-existing genetic testing records. Plan recruitment timeline accordingly.
• Signal magnitude: cardiac cfDNA signal in subclinical hATTR-related injury is expected to be substantially lower than in acute MI. Aim 1 (Step 9) is designed specifically to establish whether the signal is detectable before proceeding to Aims 2–4.
• Pre-analytical variability: deviations from the 2-hour EDTA processing window or failure to document processing time will compromise covariate adjustment in downstream analysis. Streck tubes are strongly preferred to minimize this risk.
• Bioinformatics infrastructure: CelFiE deconvolution requires computational resources beyond a standard clinical laboratory. Confirm institutional HPC or cloud computing access before sample collection begins.
Ethics statement
This protocol has not yet been initiated. No participants have been enrolled. Institutional Review Board approval and
ClinicalTrials.gov registration will be obtained prior to any participant enrollment, in accordance with the Declaration of Helsinki and applicable federal regulations. No external funding was received in the preparation of this protocol. The author declares no competing interests.
Before start
This protocol describes the complete workflow for biospecimen collection, pre-analytical processing, and tissue-of-origin deconvolution to quantify cardiac-fraction cell-free DNA (cfDNA) methylation in a proposed Phase 1 pilot study investigating preclinical detection of hereditary transthyretin amyloid cardiomyopathy (hATTR-CM). The protocol
supports four study aims: reference distribution establishment, subclinical injury detection in asymptomatic carriers, longitudinal landmark progression analysis, and exploratory treatment response monitoring.
Step 1
Participant Screening and Enrollment
Confirm participant eligibility against the five pre-defined cohort definitions: symptomatic hATTR/wtATTR-CM (no stabilizer therapy), asymptomatic hATTR mutation carrier (stabilizer-naïve), hATTR-CM on tafamidis or acoramidis, genotype-negative control, or healthy control.
For genotype-negative controls, confirm matching to an enrolled hATTR carrier within ±5 years of age, same sex, and comparable comorbidity burden.
Apply exclusion criterion: eGFR <45 mL/min/1.73m² at enrollment. Document baseline eGFR for all participants.
Document baseline ASCVD 10-year risk score for all participants. Perform coronary artery calcium (CAC) scoring where clinically feasible.
Record participant assignment to one of the five cohorts in the study database.
Step 2
Pre-Collection Participant Instructions
Instruct participant to avoid vigorous exercise for 48 hours prior to each scheduled blood draw
Schedule blood collection for morning hours, following an overnight or light fast.
Confirm participant has followed pre-collection instructions immediately prior to the draw. Document any deviations.
Step 3
Blood Collection
Collect peripheral blood (10 mL) via standard venipuncture at the scheduled timepoint: baseline (month 0), month 6, month 12, month 18, or month 24.
Collect into Streck Cell-Free DNA BCT tubes preferred) OR standard EDTA tubes if Streck tubes are unavailable.
If using EDTA tubes, record the exact time of collection. Plasma separation must occur within 2 hours - proceed immediately to Step 4.
If using Streck tubes, samples may be held at room temperature for up to 14 days prior to processing without risk of white blood cell lysis.
Record collection time of day and date in the study database — this is a required covariate for downstream analysis
Step 4
Plasma Separation - Double Centrifugation Protocol
Centrifuge whole blood at 1,600g for 10 minutes at room temperature (or 4°C if using refrigerated centrifuge) to separate plasma from cellular components.
Carefully transfer the plasma supernatant to a new tube without disturbing the cellular layer.
Centrifuge the transferred plasma a second time at 16,000g for 10 minutes to remove residual cell debris and platelets.
Transfer the clarified plasma supernatant into labeled cryovials for storage. Avoid disturbing any remaining pellet.
Record the exact time interval from blood draw (Step 3.1) to completion of first centrifugation (Step 4.1) in the study database. This processing interval is a required covariate.
Step 5
Plasma Storage and Chain of Custody
Label all plasma aliquot cryovials with participant ID, cohort assignment, timepoint, and collection date using the institutional barcode/chain-of-custody system.
Store plasma aliquots at −80°C immediately following processing. Do not use −20°C for long-term storage.
Log all aliquots in the biobank sample tracking system prior to freezer storage.
Maintain chain-of-custody documentation from collection through batch analysis.
Step 6
Cell-Free DNA Extraction
Retrieve plasma aliquots from −80°C storage in batches for cfDNA extraction, following manufacturer protocol for the selected extraction kit compatible with the downstream sequencing method (WGBS, targeted bisulfite sequencing, or EM-seq).
Quantify extracted cfDNA concentration and assess fragment size distribution using a fragment analyzer or equivalent instrument prior to library preparation.
Document cfDNA yield and quality metrics for each sample in the study database.
Step 7
Sequencing Library Preparation - Tiered Platform Strategy
For Aim 1 reference atlas construction (n = 50 samples: symptomatic hATTR/wtATTR-CM and healthy controls): prepare whole-genome bisulfite sequencing (WGBS) libraries using the Zymo-Seq Trio Kit
per manufacturer protocol.
For Aims 2–4 longitudinal surveillance (575 samples across asymptomatic carriers, treated patients, and controls): prepare targeted bisulfite sequencing libraries using the top 100–200 cardiac-specific CpG loci identified from the Aim 1 WGBS data, using Zymo Research or Daicel Arbor Biosciences myBaits Custom Methyl-Seq panels.
For future scale-up beyond this pilot: prepare enzymatic methylation sequencing (EM-seq) libraries using the NEB protocol as a lower-cost alternative with reduced DNA damage relative to bisulfite conversion.
Sequence all libraries to sufficient depth for tissue-of-origin deconvolution as specified by the CelFiE algorithm requirements.
Step 8
Tissue-of-Origin Deconvolution - CelFiE Algorithm
Process raw sequencing reads through standard quality control and alignment pipeline.
Apply the CelFiE algorithm (Caggiano et al., 2021) to deconvolute tissue-of-origin contributions from each plasma cfDNA sample.
Reference the Loyfer et al. 39-tissue methylation atlas (Nature, 2023) as the tissue reference panel, with cardiac muscle as the tissue of primary interest.
Classify individual cfDNA fragments as cardiac-origin if they exhibit concordant hypomethylation at ≥3 cardiac-specific CpG loci (including the MYL4 locus) within a single sequencing read.
Calculate the cardiac-fraction cfDNA methylation score for each sample: the percentage of total plasma cfDNA fragments meeting the cardiac-origin classification threshold in Step 8.4.
Record the cardiac-fraction score, sample ID, cohort assignment, and timepoint in the analysis database.
Step 9
Aim 1 - Reference Distribution Establishment
Compile cardiac-fraction cfDNA scores from all symptomatic hATTR/wtATTR-CM participants and healthy controls (n = 50 total).
Calculate the empirical distribution of cardiac-fraction cfDNA scores within the symptomatic disease cohort.
Determine the 95th percentile threshold from this reference distribution. This threshold will be used as the
empirically-derived definition of “elevated” cardiac cfDNA for all subsequent
aims.
Confirm assay validation: if no statistically distinguishable signal is detected between symptomatic disease and healthy controls, do not proceed to Step 10. Reassess assay sensitivity and CpG marker
selection.
Step 10
Aim 2 - Subclinical Injury Detection
Compile cardiac-fraction cfDNA scores from asymptomatic hATTR carriers and matched genotype-negative controls.
Apply covariate adjustment for age, eGFR, ASCVD 10-year risk score, and pre-analytical processing time using regression modeling.
Compare adjusted cardiac-fraction cfDNA scores between asymptomatic hATTR carriers and genotype-negative controls using the threshold established in Step 9.3.
Correlate cardiac-fraction cfDNA score with baseline global longitudinal strain (GLS) and extracellular volume fraction (ECV) on cardiac MRI.
Step 11
Aim 3 - Landmark Progression Analysis
Calculate the cardiac-fraction cfDNA trajectory for each asymptomatic hATTR carrier from baseline (month 0) to month 12 (landmark timepoint).
Adjudicate composite progression events occurring between month 12 and month 24, using a blinded imaging cardiologist with no access to cfDNA results. Composite endpoint criteria: GLS deterioration
below −18%, new LGE or ECV above 30% on CMR, septal thickness increase ≥2mm, or sustained NT-proBNP doubling.
Apply landmark survival analysis to test whether the 0→12 month cfDNA trajectory is independently associated with the 12–24 month composite progression event, adjusting for age, eGFR, and ASCVD risk.
Step 12
Aim 4 - Treatment Response Monitoring (Exploratory)
Compile cardiac-fraction cfDNA scores from hATTR-CM participants on tafamidis or acoramidis at each timepoint (months 0, 6, 12, 18, 24).
Calculate the change in cardiac-fraction cfDNA score from baseline at each subsequent timepoint.
Classify trajectory pattern: declining (pharmacodynamic evidence of cellular cardiac protection), stable/flat (disease containment), or rising (possible treatment inadequacy warranting clinical
reassessment for therapy escalation).
Document findings as hypothesis-generating; no clinical treatment decisions should be made based on Aim 4 results within this pilot study.
Step 13
Trigger-Based Safety Monitoring
At every timepoint, compare the participant's current cardiac-fraction cfDNA score to their individual baseline (month 0) value.
If the cardiac-fraction cfDNA score has risen ≥50% from individual baseline, initiate an unscheduled clinical assessment within 30 days, including echocardiography and NT-proBNP measurement.
Document all trigger-based assessments and outcomes in the study safety database.
Protocol references
•
1. Ruberg FL, Grogan M, Hanna M, et al. Transthyretin amyloid
cardiomyopathy: JACC state-of-the-art review. J Am Heart Assoc. 2023.
doi:10.1161/JAHA.123.029705.
•
2. Whelan CJ, Dungu J, Steele L, et al. Diagnostic delay and
misdiagnosis in hereditary transthyretin amyloid cardiomyopathy. Cardiol Ther.
2021. doi:10.1007/s40119-021-00219-5.
•
3. Spencer-Bonilla G, Fan J, Cheng P, et al. Timeliness of transthyretin
cardiac amyloidosis diagnosis in the Medicare population. JAMA Cardiol.
Published online April 29, 2026. doi:10.1001/jamacardio.2026.0833.
•
4. Carroll A, Dyck PJ, de Carvalho M, et al. Novel approaches to
diagnosis and management of hereditary transthyretin amyloidosis. J Neurol
Neurosurg Psychiatry. 2022;93(6):668–678. doi:10.1136/jnnp-2021-327909.
•
5. Gillmore JD, Maurer MS, Falk RH, et al. Nonbiopsy diagnosis of
cardiac transthyretin amyloidosis: AHA scientific statement. Circulation.
2020;142:e7–e22. doi:10.1161/CIR.0000000000000792.
•
6. Jacobson DR, Alexander AA, Tagoe C, Buxbaum JN. Prevalence of the
amyloidogenic transthyretin (TTR) V122I allele in 14,333 African-Americans.
Amyloid. 2015;22(3):171–174. doi:10.3109/13506129.2015.1051219.
•
7. Moss J, Zick A, Grunwald C, et al. Comprehensive human cell-type
methylation atlas reveals origins of circulating cell-free DNA in health and
disease. Nat Commun. 2018;9:5068. doi:10.1038/s41467-018-07466-6.
•
8. Loyfer N, Pasternak M, Peretz A, et al. A DNA methylation atlas of
normal human cell types. Nature. 2023;613:355–364.
doi:10.1038/s41586-022-05580-6.
•
9. Caggiano C, Beerenwinkel N, Baudis M, et al. Comprehensive cell type
decomposition of circulating cell-free DNA with CelFiE. Nat Commun.
2021;12:2675. doi:10.1038/s41467-021-22901-x.
•
10. Lehmann-Werman R, Magenheim J, Moss J, et al. Non-invasive detection
of human cardiomyocyte death using methylation patterns of circulating DNA. Nat
Commun. 2018;9:1795. doi:10.1038/s41467-018-03961-y.
•
11. Liu X, Ma J, Deng H, et al. Heart-specific DNA methylation analysis
in plasma for the investigation of myocardial damage. J Transl Med. 2022;20:44.
doi:10.1186/s12967-022-03234-9.
•
12. Derda AA, Pfanne A, Bär C, et al. Blood-based microRNA profiling in
patients with cardiac amyloidosis. PLoS One. 2018;13(10):e0204235.
doi:10.1371/journal.pone.0204235.
•
13. Ramires FJA, Moreno CR. ATTR Amyloid Cardiomyopathy:
Characterization of Extracellular Vesicles as Potential Disease Stratifiers
[NCT07314268]. ClinicalTrials.gov. 2024.
•
14. Maurer MS, Schwartz JH, Gundapaneni B, et al. Tafamidis treatment
for patients with transthyretin amyloid cardiomyopathy. N Engl J Med.
2018;379(11):1007–1016. doi:10.1056/NEJMoa1805689.
•
15. Gillmore JD, Judge DP, Cappelli F, et al. Efficacy and safety of
acoramidis in transthyretin amyloid cardiomyopathy. N Engl J Med.
2024;390(2):132–142. doi:10.1056/NEJMoa2305434.
•
16. Fontana M, Berk JL, Gillmore JD, et al. Vutrisiran in patients with
transthyretin amyloidosis with cardiomyopathy. N Engl J Med. 2025;392(1):33–44.
doi:10.1056/NEJMoa2409134.
•
17. Soman P, Garcia-Pavia P, Gillmore JD. Rationale & design of
ACT-EARLY, the acoramidis transthyretin amyloidosis prevention trial. J Nucl
Cardiol. 2024. doi:10.1016/j.nuclcard.2024.102030.
•
18. Khush KK, Patel J, Pinney S, et al. Noninvasive detection of graft
injury after heart transplant using donor-derived cell-free DNA: a prospective
multicenter study. Am J Transplant. 2019;19:2889–2899. doi:10.1111/ajt.15339.
•
19. Sperry BW, Hanna M. Changing treatment landscape in transthyretin
cardiac amyloidosis. Circ Heart Fail. 2024.
doi:10.1161/CIRCHEARTFAILURE.124.012112.
•
20. Coelho T, Wixner J, Planck T, et al. Serum transthyretin in ATTR-CM:
a pragmatic biomarker edging toward prime time. Eur J Intern Med. 2025.
doi:10.1016/j.ejim.2025.09.014.
•
21. Fontana M, Patel RK, Ioannou A, et al. Transthyretin amyloid
cardiomyopathy: natural history and treatment response assessed by
cardiovascular magnetic resonance. Eur Heart J. 2025;46:5049–5060.
doi:10.1093/eurheartj/ehaf372.
•
22. Thierry AR, El Messaoudi S, Gahan PB, et al. Origins, structures,
and functions of circulating DNA in oncology. Cancer Metastasis Rev.
2016;35(3):347–376. doi:10.1007/s10555-016-9629-x.
•
23. Guo S, et al. Cost-effective solutions for high-throughput enzymatic
DNA methylation sequencing. PMC. 2025.
•
24. Mitrani RD, Bhaskaran A, Birati EY, et al. Cell-free nucleic acids
in cardiovascular disease: from biomarkers to mechanistic drivers and
therapeutic opportunities. Cells. 2025;15(1):33. doi:10.3390/cells15010033.