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The AI agent for
lab work.

Elnora plans experiments, writes protocols, searches the literature and analyses results, helping your team discover faster inside the tools you already use.

Cortical neuron protocol

I need cortical neurons from iPSCs in three weeks. Write me the protocol.

AGENT ACTIVITY

54s
  • Analysing your request

  • Loading fair-data-protocols

  • Saving protocol(0.2s)

I have drafted and saved the 21-day NGN2 differentiation protocol, written to your lab's own conventions:

  • Seeding density: 1.5 × 10⁵ cells/cm², capped at 1.8 × 10⁵.

  • Ara-C timing: Scheduled for the Day 4 media change.

  • Feeding schedule: Half-media changes every 2 to 3 days.

  • Laminin: Included at 1 µg/mL to prevent detachment.

  • QC checkpoint: Day 14, staining for MAP2 and vGLUT1, >90% target.

  • Tracking: Always record the Matrigel lot number.

The protocol is saved in your Knowledge Base as ngn2-cortical-neuron-differentiation.md

What Elnora does

  • Give it a goal.

    Elnora plans the steps, does the work, and comes back when it needs a decision from you.

  • It knows your lab.

    Give it your protocols, results and notes. What comes back uses your reagents, your instruments and the rules your team follows.

  • It works where you do.

    Open it in a browser and point it at the files your team already keeps, so you can start with what you have today.

  • It joins your systems.

    Your own agents call Elnora as a tool over a hosted MCP server, a REST API or a command line.

BUILT FOR

Every lab, and the people who equip them.

Elnora works across life sciences, from biology and chemistry to materials, electrochemistry and physics. As long as you are performing experiments in the lab, Elnora is for you.

RESEARCH

The literature a scientist would read, read.

Every claim comes back with the source behind it, from PubMed, bioRxiv, UniProt, ChEMBL, patents and 171 scientific databases in all.

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AGENT ACTIVITY

2m 14s
  • Analysing your request

  • Loading external-search

  • Searching PubMed for recent NGN2 induction protocols(0.2s)

  • Searching OpenAlex, 20 sources(0.8s)

  • Searching Europe PMC(0.4s)

  • Searching the web, 8 papers(0.9s)

Resources used (2)

  1. 1.Shan X et al. Fully defined NGN2 neuron protocol reveals diverse signatures of neuronal maturation. Cell reports methods (2024).PMID 39255791
  2. 2.S. Fernandopulle M et al. DIFFERENTIATION OF i3NEURONS (Basic Protocol 5) v1. (2019).DOI 10.17504/PROTOCOLS.IO.5H6G39E

MEMORY

It remembers how your lab works.

Everything it writes starts from your own protocols, results and notes, kept in one place, private to you or shared with the team.

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  • archive
  • assets
  • inbox
  • inventory
  • lab-automation
  • literature
  • neuroscience
  • presentations
  • research
  • gabra5-cell-line-shortlist.md
  • hippocampal paper.pdf
cortical neuron QC checkpoint

ngn2-cortical-neuron-differentiation.md

QC Checkpoint 4 (Day 14): Immunofluorescence should confirm >90% positivity for MAP2, NeuN, and vGLUT1.

ipsc-cortical-neuron-ngn2-21day.md

QC Checkpoint 5 (Day 21): Patch-clamp or MEA recordings should detect spontaneous action potentials.

NEGATIVE DATA

The failed runs are the ones that teach it most.

The next experiment it plans starts from what went wrong last time and names the variable to change, because you gave it every run, the failures included.

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Run 014

Failed

seeded 2.4 × 10⁵ cells/cm²

sheet detachment day 4, MAP2 41% at day 14

Run 016

Failed

seeded 1.8 × 10⁵, Ara-C day 4

network thinning day 10, MAP2 68% at day 14

FILED IN THE KNOWLEDGE BASE

  • Seeding density: 1.5 × 10⁵ cells/cm², never above 1.8 × 10⁵

  • Ara-C: day 4 media change, never later

  • Feeding: half-media every 2 to 3 days

  • Laminin: 1 µg/mL

  • QC checkpoint: day 14, MAP2 at 90% or above

  • Tracking: record the Matrigel lot number

PROTOCOLS

From a one-line goal to a lab-ready protocol.

Elnora writes the steps, reagents, volumes and QC checkpoints, prints each catalogue number once it resolves to a live product page, and revises from your feedback until it is ready to run.

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ngn2-cortical-neuron-differentiation.md

v1 of 1

21-Day NGN2 Cortical Neuron Differentiation

Duration
21 days
Starting material
Human iPSCs, NGN2 inducible
Endpoint
MAP2 above 90% at day 14
  1. Day 0Seed on Matrigel1.5 × 10⁵ cells/cm²
  2. Day 0Induce with doxycycline2 µg/mL
  3. Days 1 to 3Daily full media changes
  4. Day 4Add Ara-C2 to 4 µM
  5. Days 5 to 13Half media every 2 to 3 days
  6. Day 14QC checkpoint, MAP2 and vGLUT1
  7. Days 15 to 21Continue to maturity

VERSION CONTROL

One protocol, one history, the whole team on it.

Every change to a file is kept with who made it, so you can always read the version that came before.

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ngn2-cortical-neuron-differentiation.mdv3 of 3
  1. v3Seeding lowered to 1.5 × 10⁵ after run 016Elnora, Today, 3:19 PM · current
  2. v2Ara-C moved to day 4J. Mets, Monday
  3. v1Initial versionElnora, 12 August

ANALYSIS

Results analysed and figures drawn in the same conversation.

Hand it a plate of readings and it picks the right test, checks the assumptions, draws the figure and saves it where the next person finds it.

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GABA-A α5 inhibitor, plate 7

02550751000.010.1110100IC50 2.23 µMConcentration (µM, log scale)Viability (%)

Four parameter logistic fit, R squared 0.992. A Dunnett test puts the lowest observed effect level at 0.3 µM.

THE WORK

Give it the job you would give a good postdoc.

It takes the work from the question to the file your team can use, and asks when it needs you.

  • Plan the next experiment

    Conditions, controls, replicates and the sample size the comparison actually needs.

  • Fit a dose response

    Curves from plate readings, potency with confidence intervals, compared against on-plate controls.

  • Read an instrument file

    Plate readers and potentiostats, read straight from the instrument's own export.

  • Write it up

    A document, a deck or a workbook you can edit, with the methods and figures in it.

SECURITY

Built to meet enterprise needs.

ISO/IEC 27001:2022 certified and SOC 2 Type 2 attested. Your data is never used to train models, every organisation runs isolated, and every protocol and result belongs to you.

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