ALS & Motor Neuron Disease Open Data Guide: TDP-43 Cryptic Exons, NfL Trajectories & ENCALS Models
Integrating RNA-seq cryptic splicing datasets, plasma neurofilament biomarkers, and clinical survival endpoints.
Amyotrophic Lateral Sclerosis (ALS) exhibits extreme clinical variability, with survival ranging from under 18 months to over a decade. Recent open-science advances in TDP-43 cryptic exon biology (STMN2, UNC13A), antisense oligonucleotide (ASO) trials, and plasma Neurofilament Light Chain (NfL) pharmacodynamics have revolutionized ALS trial design. This guide covers best practices for analyzing open ALS transcriptomic, genetic, and clinical datasets.
1. Detecting TDP-43 Loss-of-Function via Cryptic Exon Splicing
- Use junction-aware splicing tools (MAJIQ, LeafCutter) on high-depth RNA-seq to detect STMN2 and UNC13A cryptic exons.
- Account for UNC13A risk SNPs (rs12608932) which potentiate cryptic exon inclusion upon TDP-43 depletion.
- Benchmark clinical progression using the ENCALS multivariable survival model alongside ΔALSFRS-R and plasma NfL.
2. Clinical Trial Stratification: ALSFRS-R Slopes & Neurofilament Dynamics
Python Example: Computing Pre-Baseline ALSFRS-R Progression Rate (ΔFS) & Log-NfL Stratification
Standard Python template ready for integration into local Jupyter notebooks or automated pipelines.
import numpy as np
import pandas as pd
def compute_als_progression_metrics(df: pd.DataFrame) -> pd.DataFrame:
"""
Calculate pre-baseline progression slope (Delta-FS) and log10 plasma NfL
for ALS clinical cohort stratification (ENCALS / PRO-ACT standard).
"""
df = df.copy()
# Delta-FS = (48 - ALSFRS_R_at_baseline) / diagnostic_delay_months
df["delta_fs_slope"] = (48.0 - df["alsfrs_r_baseline"]) / df["months_since_symptom_onset"].clip(lower=1.0)
df["log10_plasma_nfl"] = np.log10(df["plasma_nfl_pg_ml"].clip(lower=1.0))
# Classify progression tier based on Delta-FS (Slow < 0.5, Intermediate 0.5-1.0, Fast > 1.0 pts/mo)
df["progression_tier"] = pd.cut(
df["delta_fs_slope"],
bins=[-np.inf, 0.5, 1.0, np.inf],
labels=["Slow (<0.5/mo)", "Intermediate (0.5-1.0/mo)", "Fast (>1.0/mo)"]
)
return dfResearch Governance & Reproducibility Checklist
- Separate bulbar-onset vs. spinal-onset ALS and genetic (SOD1, C9orf72, FUS, TARDBP) vs. sporadic cases.
- Log-transform skewed fluid biomarkers (plasma/CSF NfL and pNfH) prior to parametric regression.
- Use joint modeling of longitudinal ALSFRS-R and time-to-event survival (or CAFS) to handle informative dropout.
Key Biomarkers & Standards Referenced in This Guide
Nuclear RNA/DNA-binding protein whose cytoplasmic mislocalization, phosphorylation, and loss of nuclear splicing function drive >95% of ALS cases and ~50% of FTD.
Antioxidant enzyme gene; gain-of-toxicity mutations in SOD1 account for ~15-20% of familial ALS and are targeted by the approved antisense oligonucleotide Tofersen.
Hexanucleotide repeat expansion (GGGGCC) in C9orf72 is the most common genetic cause of both familial ALS and familial frontotemporal dementia (FTD).
Axonal cytoskeletal protein released into CSF and blood upon neuroaxonal injury; a cross-disease biomarker of neurodegeneration rate in ALS, MS, AD, and FTD.
Gold-standard 48-point clinical rating scale assessing bulbar, fine motor, gross motor, and respiratory function in ALS clinical trials.
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