5' buffer — T7 promoter — 5'UTR — Kozak — Nurr1 CDS — GGGSGGGGS linker — EGFP — stop codon — 3'UTR — 3' buffer

LOCUS       Nurr1_GFP_IVT_template       4156 bp    DNA     linear
DEFINITION  Nurr1-GFP fusion IVT template in pTwist Amp High Copy
ACCESSION   .
VERSION     .
KEYWORDS    Nurr1; GFP; IVT; mRNA; dopaminergic; PC12
SOURCE      synthetic construct
ORGANISM  synthetic construct
FEATURES             Location/Qualifiers
misc_feature    1..10
/label="5prime_buffer"
/note="AAAACCCAAA"
promoter        11..27
/label="T7_promoter"
/note="TAATACGACTCACTATA"
5'UTR           28..71
/label="5prime_UTR"
/note="Xenopus beta-globin derived, Kim et al. 2017"
regulatory      72..80
/label="Kozak"
/note="GCCACCATG"
CDS             78..1874
/label="Nurr1_CDS"
/note="rat NM_019328, stop codon removed"
/codon_start=1
misc_feature    1875..1898
/label="GGGSGGGGS_linker"
/note="GGCGGCGGCTCCGGCGGCGGCTCC"
CDS             1899..2618
/label="EGFP"
/note="AAB02572.1, Cormack et al. 1996"
misc_feature    2619..2621
/label="stop_codon"
/note="TAA"
3'UTR           2622..2715
/label="3prime_UTR"
/note="double beta-globin, Kim et al. 2017"
misc_feature    2716..2723
/label="3prime_buffer"
/note="GCGGCCGC NotI site"
ORIGIN
1 aaaacccaaa taatacgact cactataggg aaataagaga gaaaagaaga gtaagaagaa
61 atataagagc cgccaccatg cactcggctt ccagtatgct gggagccgtg aagatggaag
121 ggcacgagcc atccgactgg agcagctact acgcggagcc tgagggctac tcttccgtga
181 gcaacatgaa cgccagcctg gggatgaatg gcatgaacac ttacatgagc atgtccgcgg
241 ctgcaatggg cagtggttcc ggcaacatga gcgcaggctc catgaacatg tcatcctatg
301 tgggcgctgg aatgagcccg tcgctggctg gcatgtcccc gggcgcgggc gccatggcgg
361 gcatgagcgg ctcagctggg gcggccggcg tggcgggcat gggaccgcac ctgagtccga
421 gtctgagccc actcgggggc aggcggccgg ggctatgggt ggccttgctc cctacgccat
481 atgaactcca tgagtcctat gtacgggcag gcgggcctga gccgcgctcg ggaccccaag
541 acgtaccggc gcagctacac tcacgccaag cctccctact cgtacatctc gctcatcacc
601 atggccatcc agcagagccc caacaagatg ctgacgctga gcgagatcta tcagtggatc
661 atggacctct tccctttcta ccggcagaac cagcagcgct ggcagaactc catccgtcat
721 tctctctcct tcaacgactg ctttctcaag gtgccccgct cgccagacaa gcctggcaag
781 ggctccttct ggaccctgca ccctgactct ggcaacatgt tcgagaacgg ttgctacctg
841 cgccgccaga agcgcttcaa gtgtgagaag caactggcgt tgaaggaagc agcgggtgcg
901 ggcagtggcg gaggcaagaa gaccgctcct gggacacagg cttctcaggt tcagctcggg
961 gaggccgcag gctcggcctc tgagactccg gcgggcaccg agtcccccat tccagcgctt
1021 ctccgtgtca ggagcacaag cgaggtggcc tgagcgagct gaagggaaca cctgcctctg
1081 cgctgagtcc tccggagccg gcgccctcgc ctgggcagca gcagcaggct gcagcccacc
1141 tgctggtccc acctcaccat cctggcctgc caccagaggc ccacctgaag cccgagcacc
1201 attacgcctt caaccacccc ttctctatca acaacctcat gtcctccgag cagcaacatc
1261 atcacagcca ccaccaccat cagccccaca aaatggacct caagacctac gaacaggtca
1321 tgcactaccc tgggggctac ggttcccccatgccaggcag cttggccatg ggcccagtca
1381 cgaacaaagc cggcctggat gcctcgcccc tggctgcaga cacttcctac taccagggag
1441 tgtactccag gcctattatg aactcgtcct aaggcggcgg ctccggcggc ggctccatgg
1501 tctccaaagg tgaagaattg tttactggag ttgttccgat tctcgtggaa ctcgatggag
1561 atgtgaatgg gcataaattt tccgtcagcg gggaaggaga aggagacgca acatatggga
1621 aactcactct taaatttata tgtacaacag ggaaactccc ggttccgtgg ccaactcttg
1681 tcactactct cacatatggt gtccaatgtt ttagcaggta tcctgatcat atgaaacaac
1741 atgatttctt taaatcagca atgcctgagg gatatgtgca ggaaagaacc attttcttta
1801 aagacgatgg taattataaaacaagagctg aagtcaaatt tgagggagat acactcgtca
1861 atcggattga actcaaaggg attgacttta aagaagatgg gaatattctg ggccataaac
1921 ttgaatacaa ttataattca cataatgtgt acataatggc ggataagcaa aagaacggaa
1981 taaaagtcaa ctttaaaatt aggcataata tagaagatgg gagtgtacaa ctggcagatc
2041 attatcaaca aaatactcca attggcgatg ggccagtcct tttgcctgat aatcattatc
2101 tctcaactca aagcgctctt tcaaaggatc caaatgagaa acgagaccat atggtgttgc
2161 tcgaatttgt tactgctgct ggcattaccc ttgggatgga tgaattgtat aaataatgcc
2221 ttctgcgggg cttgccttct ggccatgccc ttcttctctc ccttgcacct gtacctcttg
2281 gtctttgaat aaagcctgag taggaagtga gggtgaagag cctgcacctc ggcgcggccgc
//

5' buffer — T7 promoter — 5'UTR — Kozak — FoxA2 CDS — GGGSGGGGS linker — mCherry — stop codon — 3'UTR — 3' buffer

LOCUS       FoxA2_RFP_IVT_template       3739 bp    DNA     linear
DEFINITION  FoxA2-mCherry fusion IVT template in pTwist Amp High Copy
ACCESSION   .
VERSION     .
KEYWORDS    FoxA2; mCherry; RFP; IVT; mRNA; dopaminergic; PC12
SOURCE      synthetic construct
ORGANISM  synthetic construct
FEATURES             Location/Qualifiers
misc_feature    1..10
/label="5prime_buffer"
promoter        11..27
/label="T7_promoter"
5'UTR           28..71
/label="5prime_UTR"
regulatory      72..80
/label="Kozak"
CDS             78..1457
/label="FoxA2_CDS"
/note="rat NM_012743, stop codon removed"
misc_feature    1458..1481
/label="GGGSGGGGS_linker"
CDS             1482..2198
/label="mCherry"
/note="Shaner et al. 2004"
misc_feature    2199..2201
/label="stop_codon"
/note="TAA"
3'UTR           2202..2295
/label="3prime_UTR"
misc_feature    2296..2303
/label="3prime_buffer"
/note="GCGGCCGC"
ORIGIN
1 aaaacccaaa taatacgact cactataggg aaataagaga gaaaagaaga gtaagaagaa
61 atataagagc cgccaccatg ctgggagcag tgaagatgga agggcacgag ccatccgact
121 ggttcagctc tggggaggcc gcaggctcgg cctctgagac tccggcgggc accgagtccc
181 cccattccag cgcttctccg tgtcaggagc acaagcgagg tggcctgagc gagctgaagg
241 gaacacctgc ctctgcgctg agtcctccgg agccggcgcc ctcgcctggg cagcagcagc
301 aggctgcagc ccacctgctg gtcccacctc accatcctgg cctgccacca gaggcccacc
361 tgaagcccga gcaccattac gccttcaacc accccttctc tatcaacaac ctcatgtcct
421 ccgagcagca acatcatcac agccaccacc accatcagcc ccacaaaatg gacctcaaga
481 cctacgaaca ggtcatgcac taccctgggg gctacggttc ccccatgcca ggcagcttgg
541 ccatgggccc agtcacgaac aaagccggcc tggatgcctc gcccctggct gcagacactt
601 cctactacca gggagtgtac tccaggccta ttatgaactc gtcctaaggc ggcggctccg
661 gcggcggctc catggtgagc aagggcgagg aggataacat ggccatcatc aaggagttca
721 tgcgcttcaa ggtgcacatg gagggctccg tgaacggcca cgagttcgag atcgagggcg
781 agggcgaggg ccgcccctac gagggcaccc agaccgccaa gctgaaggtg accaagggcg
841 gcccgctgcc cttcgcctgg gacatcctgt cccctcagtt catgtacggc tccaaggcct
901 acgtgaagca ccccgccgac atccccgact acttgaagct gtccttcccc gagggcttca
961 agtgggagcg cgtgatgaac ttcgaggacg gcggcgtggt gaccgtgacc caggactcct
1021 ccctgcagga cggcgagttc atctacaagg tgaagctgcg cggcaccaac ttcccctccg
1081 acggccccgt aatgcagaag aagaccatgg gctgggaggc ctccaccgag cggatgtacc
1141 ccgaggacgg cgccctgaag ggcgagatca agcagaggct gaagctgaag gacggcggcc
1201 actacgacgc cgaggtcaag accacctaca aggccaagaa gcccgtgcag ctgcccggcg
1261 cctacaacgt caacatcaag ctggacatca cctcccacaa cgaggactac accatcgtgg
1321 agcagtacga gcgcgccgag ggccgccact ccaccggcgg catggacgag ctgtacaagt
1381 aatgccttct gcggggcttg ccttctggcc atgcccttct tctctccctt gcacctgtac
1441 ctcttggtct ttgaataaag cctgagtagg aagtgagggt gaagagcctg cacctcggcg
1501 cggccgc
//

from opentrons import protocol_api
import time

# ============================================================
# METADATA
# ============================================================

metadata = {
    'apiLevel': '2.13',
    'protocolName': 'Organoid-lab-on-a-chip:RL-Loop',
    'author': 'Jenn Leung',
    'description': (
        'Closed-loop dopamine reinforcement learning in NGF-differentiated '
        'PC12 cells transfected with Nurr1-GFP and FoxA2-RFP mRNA. '
        'Opentrons delivers dopamine stimuli and collects media samples. '
        'ELISA reads dopamine concentration as the RL state signal.'
    )
}

# ============================================================
# EXPERIMENTAL PARAMETERS — edit these before each run
# ============================================================

# Number of RL cycles to run (each cycle = stimulus delivery + wait + sample)
N_CYCLES = 5

# Incubation time after each dopamine stimulus (minutes)
INCUBATION_MIN = 15

# Volume to sample for ELISA per well (µl)
SAMPLE_VOLUME_UL = 150

# Volume of dopamine stimulus to deliver per well (µl)
STIMULUS_VOLUME_UL = 100

# Volume of fresh media to add after each cycle (µl)
MEDIA_REPLENISH_UL = 500

# Dopamine concentration threshold for reward decision (nM)
# Set this based on your Day 18 baseline ELISA reading
REWARD_THRESHOLD_NM = 15.0

# Starting stimulus — 'low', 'medium', or 'high'
INITIAL_STIMULUS = 'low'

# Wells to treat as experimental (Nurr1-GFP + FoxA2-RFP)
EXPERIMENTAL_WELLS = ['A1', 'A2', 'A3']

# Wells to treat as controls
CONTROL_WELLS = ['B1', 'B2', 'B3']

# All wells combined
ALL_WELLS = EXPERIMENTAL_WELLS + CONTROL_WELLS

# ============================================================
# RL AGENT — Threshold Logic (Aim 1)
# ============================================================

class ThresholdRLAgent:
    """
    Simple threshold-based RL agent for Aim 1.

    Logic:
        - If dopamine above threshold → reward (escalate to next stimulus level)
        - If dopamine below threshold → withhold (maintain or reduce stimulus)

    State:  dopamine concentration (nM) from ELISA
    Action: stimulus concentration to deliver next cycle
    """

    def __init__(self, threshold_nm, initial_stimulus='low'):
        self.threshold_nm = threshold_nm
        self.stimulus_levels = {
            'none': 0,
            'low':  1,     # 1 µM dopamine
            'medium': 5,   # 5 µM dopamine
            'high': 10     # 10 µM dopamine
        }
        self.current_stimulus = initial_stimulus
        self.history = []

    def decide(self, dopamine_nm):
        """
        Given current dopamine reading, decide next stimulus.

        Args:
            dopamine_nm (float): dopamine concentration in nM from ELISA

        Returns:
            str: stimulus level for next cycle ('low', 'medium', 'high')
            float: reward value (+1, 0, or -1)
        """
        reward = 0.0

        if dopamine_nm >= self.threshold_nm:
            reward = 1.0
            # Escalate to next level if not already at max
            if self.current_stimulus == 'low':
                next_stimulus = 'medium'
            elif self.current_stimulus == 'medium':
                next_stimulus = 'high'
            else:
                next_stimulus = 'high'  # Already at max — maintain
        else:
            reward = -0.5
            # Reduce or maintain
            if self.current_stimulus == 'high':
                next_stimulus = 'medium'
            elif self.current_stimulus == 'medium':
                next_stimulus = 'low'
            else:
                next_stimulus = 'low'

        self.history.append({
            'dopamine_nm': dopamine_nm,
            'stimulus': self.current_stimulus,
            'reward': reward,
            'next_stimulus': next_stimulus
        })

        self.current_stimulus = next_stimulus
        return next_stimulus, reward

    def get_stimulus_reservoir(self, stimulus_level):
        """Return reservoir well for given stimulus level."""
        mapping = {
            'low':    'A1',   # 1 µM
            'medium': 'A2',   # 5 µM
            'high':   'A3'    # 10 µM
        }
        return mapping.get(stimulus_level, 'A1')

    def print_summary(self):
        """Print RL session summary."""
        print("\n" + "="*60)
        print("RL SESSION SUMMARY")
        print("="*60)
        print(f"Threshold: {self.threshold_nm} nM")
        print(f"Cycles completed: {len(self.history)}")
        print(f"\nCycle-by-cycle results:")
        print(f"{'Cycle':>6} {'Dopamine (nM)':>14} {'Stimulus':>10} {'Reward':>8} {'Next':>10}")
        print("-"*55)
        for i, h in enumerate(self.history):
            print(
                f"{i+1:>6} "
                f"{h['dopamine_nm']:>14.1f} "
                f"{h['stimulus']:>10} "
                f"{h['reward']:>8.1f} "
                f"{h['next_stimulus']:>10}"
            )
        total_reward = sum(h['reward'] for h in self.history)
        print(f"\nTotal reward: {total_reward:.1f}")
        print("="*60)


# ============================================================
# RL AGENT — Q-Learning (Aim 2 extension)
# ============================================================

class QLearningRLAgent:
    """
    Q-learning agent for Aim 2 — more sophisticated RL.

    State space:  dopamine concentration binned into discrete levels
    Action space: stimulus concentrations (none, low, medium, high)

    Q-table updated after each cycle using Bellman equation.
    Epsilon-greedy exploration allows agent to occasionally try
    non-optimal actions to discover better strategies.
    """

    def __init__(self, threshold_nm, alpha=0.1, gamma=0.9, epsilon=0.2):
        """
        Args:
            threshold_nm:  reward threshold in nM
            alpha:         learning rate (0-1) — how fast Q-values update
            gamma:         discount factor (0-1) — how much future rewards matter
            epsilon:       exploration rate (0-1) — random action probability
        """
        import numpy as np
        self.np = np
        self.threshold_nm = threshold_nm
        self.alpha = alpha
        self.gamma = gamma
        self.epsilon = epsilon

        # State bins — dopamine concentration ranges (nM)
        self.state_bins = [0, 5, 10, 20, 40, 80, float('inf')]
        self.n_states = len(self.state_bins) - 1

        # Actions
        self.actions = ['none', 'low', 'medium', 'high']
        self.n_actions = len(self.actions)

        # Q-table initialised to zero
        self.q_table = np.zeros((self.n_states, self.n_actions))

        self.history = []
        self.current_state = 0

    def get_state(self, dopamine_nm):
        """Bin continuous dopamine reading into discrete state."""
        for i in range(len(self.state_bins) - 1):
            if self.state_bins[i] <= dopamine_nm < self.state_bins[i + 1]:
                return i
        return self.n_states - 1

    def get_reward(self, dopamine_before, dopamine_after):
        """
        Reward based on change in dopamine release.

        Positive reward for increase above threshold.
        Negative reward for decrease.
        """
        delta = dopamine_after - dopamine_before
        if dopamine_after >= self.threshold_nm and delta > 0:
            return 1.0
        elif delta > 5:
            return 0.5
        elif delta < -5:
            return -0.5
        else:
            return 0.0

    def choose_action(self, state):
        """Epsilon-greedy action selection."""
        import random
        if random.random() < self.epsilon:
            return random.randint(0, self.n_actions - 1)
        return int(self.np.argmax(self.q_table[state]))

    def update(self, state, action_idx, reward, next_state):
        """Q-learning update rule (Bellman equation)."""
        current_q = self.q_table[state, action_idx]
        max_next_q = self.np.max(self.q_table[next_state])
        new_q = current_q + self.alpha * (
            reward + self.gamma * max_next_q - current_q
        )
        self.q_table[state, action_idx] = new_q

    def decide(self, dopamine_before, dopamine_after):
        """Full Q-learning update and next action decision."""
        state = self.get_state(dopamine_before)
        next_state = self.get_state(dopamine_after)
        reward = self.get_reward(dopamine_before, dopamine_after)

        # Get action that was taken (stored from previous cycle)
        action_idx = getattr(self, '_last_action_idx', 1)

        # Update Q-table
        self.update(state, action_idx, reward, next_state)

        # Choose next action
        next_action_idx = self.choose_action(next_state)
        self._last_action_idx = next_action_idx
        next_action = self.actions[next_action_idx]

        self.history.append({
            'dopamine_before': dopamine_before,
            'dopamine_after': dopamine_after,
            'state': state,
            'next_state': next_state,
            'reward': reward,
            'next_action': next_action
        })

        return next_action, reward

    def get_stimulus_reservoir(self, stimulus_level):
        mapping = {
            'none':   None,
            'low':    'A1',
            'medium': 'A2',
            'high':   'A3'
        }
        return mapping.get(stimulus_level)

    def print_q_table(self):
        """Print current Q-table values."""
        print("\nQ-TABLE (rows=states, cols=actions)")
        print(f"{'State (nM)':>15}", end="")
        for a in self.actions:
            print(f"{a:>10}", end="")
        print()
        state_labels = ['0-5', '5-10', '10-20', '20-40', '40-80', '80+']
        for i, label in enumerate(state_labels):
            print(f"{label:>15}", end="")
            for j in range(self.n_actions):
                print(f"{self.q_table[i,j]:>10.3f}", end="")
            print()


# ============================================================
# MAIN PROTOCOL
# ============================================================

def run(protocol: protocol_api.ProtocolContext):

    # ----------------------------------------------------------
    # LABWARE SETUP
    # ----------------------------------------------------------

    # Tip racks
    tiprack_1 = protocol.load_labware(
        'opentrons_96_tiprack_300ul', 4,
        label='Tip Rack 1'
    )
    tiprack_2 = protocol.load_labware(
        'opentrons_96_tiprack_300ul', 5,
        label='Tip Rack 2'
    )

    # Reagent reservoir (NEST 12-well)
    reservoir = protocol.load_labware(
        'nest_12_reservoir_15ml', 1,
        label='Reagent Reservoir'
    )

    # PC12 cell plate (6-well)
    cell_plate = protocol.load_labware(
        'corning_6_wellplate_16.8ml_flat', 2,
        label='PC12 Cell Plate'
    )

    # ELISA sample collection plate (96-well flat)
    elisa_plate = protocol.load_labware(
        'corning_96_well_plate_360ul_flat', 3,
        label='ELISA Sample Plate'
    )

    # ----------------------------------------------------------
    # REAGENT POSITIONS
    # ----------------------------------------------------------

    dopamine_1uM  = reservoir['A1']   # Low stimulus
    dopamine_5uM  = reservoir['A2']   # Medium stimulus
    dopamine_10uM = reservoir['A3']   # High stimulus
    pbs_wash      = reservoir['A4']   # PBS wash
    fresh_medium  = reservoir['A5']   # Fresh NGF differentiation medium
    waste         = reservoir['A6']   # Liquid waste

    stimulus_wells = {
        'low':    dopamine_1uM,
        'medium': dopamine_5uM,
        'high':   dopamine_10uM
    }

    # ----------------------------------------------------------
    # PIPETTE SETUP
    # ----------------------------------------------------------

    p300 = protocol.load_instrument(
        'p300_single_gen2',
        mount='right',
        tip_racks=[tiprack_1, tiprack_2]
    )

    # ----------------------------------------------------------
    # ELISA PLATE MAP
    # Row A = baseline (cycle 0)
    # Row B = cycle 1
    # Row C = cycle 2 ... etc.
    # Columns 1-3 = experimental wells A1,A2,A3
    # Columns 4-6 = control wells B1,B2,B3
    # ----------------------------------------------------------

    elisa_row_letters = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H']

    def get_elisa_well(cycle, well_index):
        """
        Get ELISA plate well for a given cycle and cell plate well.
        cycle 0 = baseline, cycle 1-5 = post-stimulus
        well_index 0-5 corresponds to A1,A2,A3,B1,B2,B3
        """
        row = elisa_row_letters[cycle]
        col = well_index + 1
        return elisa_plate[f'{row}{col}']

    # ----------------------------------------------------------
    # RL AGENT INITIALISATION
    # Change QLearningRLAgent to ThresholdRLAgent for Aim 1
    # ----------------------------------------------------------

    agent = ThresholdRLAgent(
        threshold_nm=REWARD_THRESHOLD_NM,
        initial_stimulus=INITIAL_STIMULUS
    )

    # ----------------------------------------------------------
    # HELPER FUNCTIONS
    # ----------------------------------------------------------

    def collect_sample(cell_well, elisa_well, volume_ul):
        """
        Collect conditioned medium sample from cell well
        and transfer to ELISA plate.
        Always uses a fresh tip.
        """
        p300.pick_up_tip()
        p300.aspirate(volume_ul, cell_plate[cell_well].bottom(z=2))
        p300.dispense(volume_ul, elisa_well)
        p300.blow_out(elisa_well)
        p300.drop_tip()

    def deliver_stimulus(cell_well, stimulus_level, volume_ul):
        """
        Deliver dopamine stimulus to cell well.
        Always uses a fresh tip.
        """
        if stimulus_level == 'none':
            protocol.comment(f'  Withholding stimulus for {cell_well}')
            return

        source = stimulus_wells[stimulus_level]
        p300.pick_up_tip()
        p300.aspirate(volume_ul, source)
        p300.dispense(volume_ul, cell_plate[cell_well].bottom(z=3))
        # Gentle mix — 3 times at 80 µl to avoid disturbing cells
        p300.mix(3, 80, cell_plate[cell_well].bottom(z=3))
        p300.blow_out(cell_plate[cell_well].top(z=-5))
        p300.drop_tip()

    def replenish_medium(cell_well, volume_ul):
        """
        Add fresh differentiation medium + NGF to well.
        Always uses a fresh tip.
        """
        p300.pick_up_tip()
        # Aspirate from side to avoid disturbing cells/neurites
        p300.aspirate(volume_ul, fresh_medium)
        # Dispense gently to side of well, not directly onto cells
        p300.dispense(volume_ul, cell_plate[cell_well].bottom(z=5))
        p300.drop_tip()

    def get_dopamine_reading():
        """
        Pause protocol and prompt user to enter ELISA reading.
        In Aim 2, this would be replaced by a direct plate reader API call.

        Returns:
            float: dopamine concentration in nM
        """
        protocol.pause(
            'ELISA READING REQUIRED\n'
            'Run ELISA on collected samples now.\n'
            'Note dopamine concentration (nM) for experimental wells.\n'
            'Resume protocol when ready.'
        )
        # In a real run, you would either:
        # (a) manually note the reading and resume
        # (b) integrate with Spark Plate Reader API to read automatically
        # Returning a placeholder here — replace with actual reading logic
        # For automated integration:
        # from spark_api import SparkReader
        # reader = SparkReader()
        # reading = reader.read_absorbance(plate=elisa_plate, wavelength=450)
        # dopamine_nm = calculate_from_standard_curve(reading)
        # return dopamine_nm
        return 0.0  # Replace with actual reading

    # ----------------------------------------------------------
    # PROTOCOL EXECUTION
    # ----------------------------------------------------------

    protocol.comment("="*60)
    protocol.comment("ASSEMBLOID AGENCY — BIOCHEMICAL RL LOOP")
    protocol.comment(f"Cycles: {N_CYCLES}")
    protocol.comment(f"Incubation per cycle: {INCUBATION_MIN} min")
    protocol.comment(f"Reward threshold: {REWARD_THRESHOLD_NM} nM")
    protocol.comment(f"Initial stimulus: {INITIAL_STIMULUS}")
    protocol.comment("="*60)

    # ----------------------------------------------------------
    # STEP 1: COLLECT BASELINE SAMPLES (Cycle 0)
    # ----------------------------------------------------------

    protocol.comment("\n--- BASELINE SAMPLING (Cycle 0) ---")

    all_wells = EXPERIMENTAL_WELLS + CONTROL_WELLS

    for well_idx, well in enumerate(all_wells):
        elisa_dest = get_elisa_well(cycle=0, well_index=well_idx)
        protocol.comment(f'  Collecting baseline from {well} → ELISA {elisa_dest}')
        collect_sample(well, elisa_dest, SAMPLE_VOLUME_UL)

    # Prompt for baseline ELISA reading
    protocol.pause(
        'BASELINE ELISA\n'
        'Run ELISA on Row A of sample plate.\n'
        'Record baseline dopamine (nM) for each well.\n'
        'This sets your reference point for RL decisions.\n'
        'Resume when ready.'
    )

    # ----------------------------------------------------------
    # STEP 2: DELIVER INITIAL STIMULUS
    # ----------------------------------------------------------

    protocol.comment(f"\n--- INITIAL STIMULUS DELIVERY ({INITIAL_STIMULUS}) ---")

    current_stimulus = INITIAL_STIMULUS

    for well in EXPERIMENTAL_WELLS:
        protocol.comment(f'  Delivering {current_stimulus} dopamine to {well}')
        deliver_stimulus(well, current_stimulus, STIMULUS_VOLUME_UL)

    # Controls receive PBS instead of dopamine
    for well in CONTROL_WELLS:
        protocol.comment(f'  PBS to control well {well}')
        p300.pick_up_tip()
        p300.aspirate(STIMULUS_VOLUME_UL, pbs_wash)
        p300.dispense(STIMULUS_VOLUME_UL, cell_plate[well].bottom(z=3))
        p300.drop_tip()

    # ----------------------------------------------------------
    # STEP 3: RL CYCLES
    # ----------------------------------------------------------

    dopamine_before = REWARD_THRESHOLD_NM  # Starting estimate — updated by ELISA

    for cycle in range(1, N_CYCLES + 1):

        protocol.comment(f"\n{'='*60}")
        protocol.comment(f"RL CYCLE {cycle} of {N_CYCLES}")
        protocol.comment(f"Current stimulus: {current_stimulus}")
        protocol.comment(f"{'='*60}")

        # ---- INCUBATION ----
        protocol.comment(f"  Incubating {INCUBATION_MIN} minutes...")
        protocol.delay(
            minutes=INCUBATION_MIN,
            msg=f'RL Cycle {cycle} — stimulus incubation. '
                f'Keep cells on deck. Resume automatically.'
        )

        # ---- COLLECT POST-STIMULUS SAMPLES ----
        protocol.comment(f"  Collecting post-stimulus samples (Cycle {cycle})...")

        for well_idx, well in enumerate(all_wells):
            elisa_dest = get_elisa_well(cycle=cycle, well_index=well_idx)
            protocol.comment(f'    {well} → ELISA {elisa_dest}')
            collect_sample(well, elisa_dest, SAMPLE_VOLUME_UL)

        # ---- GET ELISA READING ----
        # In Aim 1: manual ELISA run, user enters reading, resumes
        # In Aim 2: automated plate reader API call
        dopamine_after = get_dopamine_reading()

        # ---- RL DECISION ----
        protocol.comment(f"\n  RL DECISION:")
        protocol.comment(f"  Dopamine before: {dopamine_before:.1f} nM")
        protocol.comment(f"  Dopamine after:  {dopamine_after:.1f} nM")

        next_stimulus, reward = agent.decide(dopamine_after)

        protocol.comment(f"  Reward:          {reward:.1f}")
        protocol.comment(f"  Next stimulus:   {next_stimulus}")

        # ---- REPLENISH MEDIUM ----
        protocol.comment("  Replenishing medium...")
        for well in all_wells:
            replenish_medium(well, MEDIA_REPLENISH_UL)

        # ---- DELIVER NEXT STIMULUS ----
        if cycle < N_CYCLES:
            protocol.comment(f"  Delivering {next_stimulus} stimulus...")
            for well in EXPERIMENTAL_WELLS:
                deliver_stimulus(well, next_stimulus, STIMULUS_VOLUME_UL)
            for well in CONTROL_WELLS:
                p300.pick_up_tip()
                p300.aspirate(STIMULUS_VOLUME_UL, pbs_wash)
                p300.dispense(STIMULUS_VOLUME_UL, cell_plate[well].bottom(z=3))
                p300.drop_tip()

        # Update tracking variables
        dopamine_before = dopamine_after
        current_stimulus = next_stimulus

    # ----------------------------------------------------------
    # STEP 4: FINAL SUMMARY
    # ----------------------------------------------------------

    agent.print_summary()

    protocol.comment("\n" + "="*60)
    protocol.comment("RL SESSION COMPLETE")
    protocol.comment(f"Total cycles completed: {N_CYCLES}")
    protocol.comment("ELISA sample plate is ready for final analysis.")
    protocol.comment("Return cells to incubator if further culture needed.")
    protocol.comment("="*60)

    protocol.pause(
        'SESSION COMPLETE\n'
        'Collect ELISA sample plate for final analysis.\n'
        'Return PC12 cell plate to incubator.\n'
        'Check RL summary printed to console.'
    )


# ============================================================
# STANDALONE SIMULATION MODE
# Run this file directly (not via Opentrons App) to simulate
# the RL agent logic without hardware
# ============================================================

if __name__ == '__main__':
    """
    Simulate the RL agent logic offline.
    Useful for testing threshold and Q-learning parameters
    before running on the actual OT-2.

    Usage:
        python assembloid_rl_protocol.py
    """

    print("ASSEMBLOID AGENCY — RL Agent Simulation (no hardware)")
    print("="*60)

    # Simulated dopamine readings (nM) — replace with real ELISA values
    simulated_readings = [8.2, 12.4, 18.6, 24.3, 38.7, 41.2]

    # Test threshold agent
    print("\n--- THRESHOLD AGENT SIMULATION ---")
    threshold_agent = ThresholdRLAgent(
        threshold_nm=15.0,
        initial_stimulus='low'
    )

    for i, reading in enumerate(simulated_readings[1:], 1):
        stimulus, reward = threshold_agent.decide(reading)
        print(
            f"Cycle {i}: "
            f"dopamine={reading:.1f} nM | "
            f"reward={reward:.1f} | "
            f"next stimulus={stimulus}"
        )

    threshold_agent.print_summary()

    # Test Q-learning agent
    print("\n--- Q-LEARNING AGENT SIMULATION ---")
    try:
        import numpy as np
        q_agent = QLearningRLAgent(
            threshold_nm=15.0,
            alpha=0.1,
            gamma=0.9,
            epsilon=0.2
        )
        q_agent._last_action_idx = 1  # Start with 'low'

        readings_before = simulated_readings[:-1]
        readings_after = simulated_readings[1:]

        for i, (before, after) in enumerate(
            zip(readings_before, readings_after), 1
        ):
            action, reward = q_agent.decide(before, after)
            print(
                f"Cycle {i}: "
                f"before={before:.1f} nM | "
                f"after={after:.1f} nM | "
                f"reward={reward:.1f} | "
                f"next={action}"
            )

        q_agent.print_q_table()

    except ImportError:
        print("numpy not available — skipping Q-learning simulation")
        print("Install with: pip install numpy")
        

##### Experimental Protocol
###### 1. Overview and Scientific Rationale
This protocol describes a complete experime
ntal workflow for: (1) generating synthetic mRNA encoding the dopaminergic transcription factors Nurr1 and FoxA2; (2) transfecting NGF-differentiated PC12 rat pheochromocytoma cells; (3) verifying dopaminergic differentiation via dopamine ELISA and TH immunostaining; and (4) implementing a minimal closed-loop chemical reinforcement learning (RL) system using the Opentrons liquid handling robot.


The approach is based on Kim et al. (2017) who demonstrated that synthetic mRNA transfection of Nurr1 and FoxA2 into rat neural precursor cells generates functional dopaminergic neurons exhibiting electrophysiological and biochemical properties characteristic of midbrain dopamine neurons. This protocol adapts their methodology for PC12 cells as a BSL-1 compatible, immortalised dopaminergic model system.


###### 1.1 Scientific Background


PC12 cells are an immortalised rat adrenal pheochromocytoma cell line that synthesise and store catecholamines, predominantly dopamine. Upon NGF treatment they extend neurites and acquire a sympathetic neuron-like phenotype. The addition of Nurr1 and FoxA2 mRNA pushes these cells further toward authentic midbrain dopaminergic identity by activating the full dopamine synthesis machinery including tyrosine hydroxylase (TH), dopamine transporter (DAT), and vesicular monoamine transporter 2 (VMAT2).


###### 1.2 Chemical Reinforcement Learning Concept


The closed-loop RL system uses Opentrons to deliver dopamine pulses as reward signals to the differentiated PC12 cell culture. Dopamine release from cells is measured by ELISA as the state readout. If dopamine release exceeds a defined threshold following a stimulus, a reward pulse is delivered. This creates a minimal biological RL loop in which cellular dopaminergic activity is the state variable and exogenous dopamine delivery is the reward signal.


###### 2. Safety


###### 2.1 Biosafety Classification


All components of this protocol are BSL-1 safe:


###### 2.2 Personal Protective Equipment


Lab coat, nitrile gloves, and eye protection at all times


All cell work performed in laminar flow biosafety cabinet (Class II)


Dopamine: prepare in fume hood, avoid skin contact, protect from light


RNA work: always use RNase-free consumables, change gloves frequently


All liquid waste decontaminated with 10% bleach before disposal


###### 3. Materials and Equipment


###### 3.1 Cell Line


###### 3.2 Constructs (from Twist Bioscience)


NOTE: All constructs use Kim et al. (2017) UTR design: T7 promoter + Xenopus 5'UTR + Kozak + CDS + custom 3'UTR. Poly-A tail added enzymatically post-IVT.


###### 3.3 Reagents


###### 3.4 Equipment


Laminar flow biosafety cabinet (Class II)


CO2 incubator (37°C, 5% CO2)


Centrifuge


Plate reader (absorbance and fluorescence)


Opentrons OT-2 or Flex with P300 single channel pipette


Fluorescence microscope


Nanodrop spectrophotometer


Water bath (37°C)


Thermal cycler (for optional PCR verification)


Agarose gel electrophoresis system


###### 3.5 Consumables


6-well plates (PDL/laminin coated)


96-well plates (for ELISA)


T25 and T75 flasks


RNase-free tubes and tips (all RNA work)


Standard filtered pipette tips


15 ml and 50 ml centrifuge tubes


Cryovials (for cell stock preparation)


###### 4. Media and Reagent Preparation


###### 4.1 Growth Medium


DMEM high glucose


10% horse serum (heat inactivated)


5% FBS (heat inactivated)


1% Penicillin/Streptomycin


NOTE: Warm to 37°C before use. Store at 4°C for up to 4 weeks.


###### 4.2 Differentiation Medium


DMEM high glucose


1% horse serum (heat inactivated)


1% Penicillin/Streptomycin


50 ng/ml NGF 2.5S (add fresh at each media change)


NOTE: Prepare NGF working stock fresh from aliquots. Never refreeze thawed NGF.


######  4.3 NGF Stock Solution


Reconstitute NGF in sterile PBS + 0.1% BSA to 100 µg/ml


Aliquot into single-use volumes (typically 5-10 µl)


Store at -80°C


Dilute to working concentration (50-100 ng/ml) in differentiation medium immediately before use


######  4.4 Dopamine Working Solution


Dissolve dopamine hydrochloride in sterile PBS to 10 mM stock


Add ascorbic acid to 0.1 mg/ml final concentration


Prepare fresh on day of experiment


Keep on ice, protect from light, use within 4 hours


Prepare working dilutions: 1 µM, 5 µM, 10 µM in differentiation medium


NOTE: Dopamine oxidises rapidly. Yellow/brown discolouration indicates degradation — discard and prepare fresh.


######  5. Part 1 — Bacterial Transformation and Plasmid Preparation


Timeline: Days 1-4. Perform for all three constructs simultaneously.


###### 5.1 Transformation (Day 1)


Remove NEB 10-beta competent cells from -80°C, thaw on ice 10 minutes


Add 1-2 µl plasmid DNA (from Twist delivery) to competent cells


Flick gently to mix — do not vortex


Incubate on ice 30 minutes


Heat shock at 42°C for exactly 45 seconds


Return immediately to ice for 2 minutes


Add 250 µl LB broth (no ampicillin)


Incubate at 37°C for 1 hour shaking at 200 rpm


Spread onto LB agar + ampicillin (100 µg/ml) plates


Incubate overnight at 37°C


###### 5.2 Colony Picking and Overnight Culture (Day 2)


Check plates — expect 10-100 colonies per construct


Pick 3-6 individual well-separated colonies per construct


Inoculate each into 5 ml LB broth + ampicillin (100 µg/ml) in 15 ml tube


Grow overnight at 37°C shaking at 200 rpm


###### 5.3 Miniprep (Day 3)


Follow GeneJET Miniprep Kit protocol exactly


Elute in 50 µl elution buffer


Measure concentration by Nanodrop — aim for >50 ng/µl


Check A260/280 ratio — should be 1.8-2.0


Send one sample per construct for Sanger sequencing


Store at -20°C


###### 5.4 Sequencing Verification (Day 4)


Align sequencing results against Benchling sequences. Confirm:


T7 promoter intact: TAATACGACTCACTATA


5'UTR intact: AAATAAGAGAGAAAAGAAGAGTAAGAAGAAATATAAGAGCC


Kozak + ATG intact: GCCACCATG


CDS correct with no frameshifts or premature stop codons


3'UTR intact


NOTE: Do not proceed to IVT until sequencing confirms all three constructs are correct.


###### 6. Part 2 — In Vitro Transcription (IVT) and mRNA Preparation


Timeline: Day 5. Perform in RNase-free conditions throughout. Change gloves frequently.


######  6.1 Template Linearisation


Set up KpnI digestion for each construct:


2 µg plasmid DNA


2 µl CutSmart Buffer (10x)


1 µl KpnI enzyme


Make up to 20 µl with RNase-free water


Incubate 1 hour at 37°C


Heat inactivate 20 minutes at 65°C


Verify linearisation on 1% agarose gel — should show single linear band


Purify linearised template using GeneJET PCR Purification Kit


Elute in RNase-free water, quantify by Nanodrop


NOTE: Linearisation is critical — circular template produces run-on transcripts that reduce mRNA quality.


###### 6.2 IVT Reaction (HiScribe T7 Kit)


Following Kim et al. (2017) protocol with HiScribe T7 High Yield RNA Synthesis Kit:


Assemble on ice in RNase-free tube:


Mix gently by flicking — do not vortex


Incubate at 37°C for 2 hours


Add 2 µl TURBO DNase, mix gently, incubate 15 minutes at 37°C


NOTE: DNase step removes DNA template — essential to prevent DNA contamination of your mRNA product.


###### 6.3 Poly-A Tailing (E. coli Poly-A Polymerase)


Following Kim et al. (2017) — poly-A added enzymatically post-IVT:


To the 20 µl IVT product add:


4 µl 10x E. coli Poly-A Polymerase Reaction Buffer


2 µl ATP (10 mM)


2 µl E. coli Poly-A Polymerase


12 µl RNase-free water (total reaction 40 µl)


Incubate 45 minutes at 37°C


NOTE: This enzymatically adds ~150-200 A's — better than DNA-encoded poly-A for translation efficiency.


###### 6.4 mRNA Purification (RNeasy Kit)


Follow RNeasy Mini Kit protocol for RNA cleanup


Elute in 50 µl RNase-free water


Quantify by Nanodrop — note A260/280 and A260/230 ratios


Run 1 µl on agarose gel to verify mRNA integrity — should show single band


Aliquot into single-use volumes


Store at -80°C


NOTE: Expected yield: 50-100 µg mRNA per 20 µl IVT reaction. A260/280 should be ~2.0.


###### 7. Part 3 — PC12 Cell Culture and NGF Differentiation


Timeline: Days 1-14. Run in parallel with bacterial/IVT work.


###### 7.1 Thawing PC12 Cells (Day 1)


Warm growth medium to 37°C


Remove PC12 vial from liquid nitrogen, thaw quickly in 37°C water bath


Transfer dropwise into 9 ml warm growth medium in 15 ml tube — add medium slowly


Centrifuge 200 x g for 5 minutes


Aspirate supernatant carefully


Resuspend pellet in 5 ml warm growth medium


Transfer to T25 flask


Incubate at 37°C, 5% CO2


Check next day — cells should be attached


###### 7.2 Plate Coating (Day 7)


Prepare PDL solution: 50 µg/ml in sterile water


Add 1 ml per well of 6-well plate


Incubate 1 hour at room temperature


Aspirate PDL completely


Wash 3x with sterile water


Allow to dry completely in laminar flow hood


Prepare laminin solution: 10 µg/ml in PBS


Add 1 ml per well


Incubate overnight at 37°C


NOTE: PDL coating is essential for PC12 Adh adhesion. Laminin significantly improves neurite extension and process stability.


###### 7.3 Cell Plating and Differentiation (Day 8)


Aspirate laminin solution — do not wash, leave residual laminin


Trypsinise PC12 cells from flask


Count using haemocytometer


Plate at 50,000 cells/well in growth medium


Incubate overnight


Day 9: Replace with differentiation medium containing 50 ng/ml NGF


Days 9-15: Change half the medium every 2-3 days with fresh NGF


NOTE: Always add medium to the side of the well — never directly onto cells. Neurites are fragile.


###### 8. Part 4 — mRNA Transfection


Timeline: Day 15 onwards. Daily transfection for 5-7 days following Kim et al. (2017).


###### 8.1 Experimental Groups


###### 8.2 Daily Transfection Protocol (Kim et al. Method)


Perform daily for 5-7 consecutive days starting Day 15:


###### Prepare mRNA-MessengerMAX complex (per well):


Dilute mRNA in Opti-MEM: 500 ng total mRNA in 25 µl Opti-MEM


For Nurr1 + FoxA2 wells: 250 ng Nurr1 mRNA + 250 ng FoxA2 mRNA


For GFP wells: 500 ng GFP mRNA


Dilute MessengerMAX: 1.5 µl MessengerMAX in 25 µl Opti-MEM


Incubate separately 5 minutes at room temperature


Combine mRNA and MessengerMAX dilutions


Mix gently by pipetting 3-4 times — do not vortex


Incubate 5 minutes at room temperature


###### Transfect cells:


Aspirate half the medium from each well carefully


Add transfection complex dropwise to cells


Rock plate gently to distribute


Incubate 4 hours at 37°C, 5% CO2


Replace with fresh differentiation medium + NGF


NOTE: Following Kim et al. (2017): daily transfection is required because mRNA degrades within 24-48 hours. Adding db-cAMP (0.5 mM) alongside transfection significantly improves expression efficiency in rat cells — add to differentiation medium if available.


###### 8.3 Transfection Timeline


###### 9. Part 5 — Verification of Dopaminergic Differentiation


###### 9.1 TH Immunostaining


Tyrosine hydroxylase (TH) is the rate-limiting enzyme in dopamine synthesis. Increased TH expression confirms successful dopaminergic differentiation.


Aspirate medium and wash cells 2x with PBS


Fix with 4% paraformaldehyde for 15 minutes at room temperature


Wash 3x with PBS


Permeabilise with 0.25% Triton X-100 in PBS for 10 minutes


Wash 3x with PBS


Block with 3% BSA in PBS for 1 hour at room temperature


Add anti-TH primary antibody (1:500 in blocking buffer)


Incubate overnight at 4°C


Wash 3x with PBS


Add FITC-conjugated secondary antibody (1:500) for 1 hour at room temperature in dark


Wash 3x with PBS


Add DAPI (1:1000) for 5 minutes


Wash 2x with PBS


Image on fluorescence microscope


Positive result: increased TH+ green fluorescence in Nurr1 + FoxA2 wells compared to GFP control.


###### 9.2 Dopamine ELISA


Primary readout for both differentiation verification and chemical RL loop. Follows methodology used in the PC12 dopamine literature (Eagle Biosciences High Sensitivity Dopamine ELISA Kit).


At least 2 hours before ELISA: replace medium with 1 ml fresh DMEM without serum


Stimulate with 56 mM KCl for 15 minutes to trigger dopamine release


Collect conditioned medium into RNase-free tube


Centrifuge at 300 x g for 5 minutes to remove cell debris


Transfer supernatant — use immediately or store at -80°C


Follow Eagle Biosciences ELISA kit protocol exactly


Read plate at 450 nm on plate reader


Calculate dopamine concentration from standard curve


###### 10. Part 6 — Chemical Reinforcement Learning with Opentrons


Timeline: Day 21. Perform after dopamine ELISA confirms differentiation is successful.


###### 10.1 Opentrons Deck Setup


###### 10.2 RL Loop Logic


The Opentrons Python script implements the following logic:


Baseline read: collect 50 µl conditioned medium, transfer to ELISA plate


Define reward threshold: dopamine concentration above X nM (determined from Day 18 baseline ELISA)


Deliver stimulus: Opentrons dispenses 100 µl dopamine solution (start at 1 µM)


Incubation: 15 minutes


State read: collect medium sample for ELISA


Decision: if dopamine above threshold → escalate to 5 µM; if below → maintain or withhold


Repeat for 3-5 cycles per session


###### 10.3 Opentrons Python Protocol

###### 10.4 Data Recording


Record for every RL session:


Date and time of each dopamine delivery


Dopamine concentration dispensed (µM)


Volume delivered (µl)


ELISA dopamine reading (nM) before and after each pulse


Cell passage number


Days post transfection


Days post NGF differentiation


Any observations about cell morphology


###### 11. Troubleshooting


###### 12. Complete Experiment Timeline


###### 13. Key References


1. Kim S et al. (2017) Efficient Generation of Dopamine Neurons by Synthetic Transcription Factor mRNAs. Molecular Therapy. PMC5589083.


2. Sari Y, Sousa Rosa S et al. (2024) Comprehensive evaluation of T7 promoter for enhanced yield and quality in mRNA production. Scientific Reports. PMC11053036.


3. Lee HS et al. (2010) Foxa2 and Nurr1 Synergistically Yield A9 Nigral Dopamine Neurons Exhibiting Improved Differentiation, Function, and Cell Survival. Stem Cells. PMID:20049900.


4. Yang K et al. (2019) Synaptic dopamine release is positively regulated by SNAP-25 that involves in benzo[a]pyrene-induced neurotoxicity. Chemosphere 237:124378.


5. Homberg JR et al. (2016) The role of the dopamine D1 receptor in social cognition: studies using a novel genetic rat model. Disease Models & Mechanisms 9:1147-1158.


6. Wiatrak B et al. (2020) PC12 Cell Line: Cell Types, Coating of Culture Vessels, Differentiation and Other Culture Conditions. Cells 9(4):958.


7. Greene LA, Tischler AS (1976) Establishment of a noradrenergic clonal line of rat adrenal pheochromocytoma cells which respond to nerve growth factor. PNAS 73:2424-2428.


###### 14. Experimental Notes
###### Observations:
| RL Component | Biological Implementation |
| --- | --- |
| State | Dopamine concentration measured by ELISA from conditioned media |
| Action | Opentrons delivers dopamine pulse to culture well |
| Reward signal | Dopamine above threshold triggers further stimulation |
| Suppression signal | Below threshold — withhold stimulus |
| Learning readout | Shift in baseline dopamine release over multiple sessions |


| Component | BSL Classification | Notes |
| --- | --- | --- |
| PC12 cells (CRL-1721) | BSL-1 | Rat, non-human, non-primate |
| Nurr1 mRNA | BSL-1 | Synthetic mRNA, no pathogen sequences |
| FoxA2 mRNA | BSL-1 | Synthetic mRNA, no pathogen sequences |
| GFP mRNA | BSL-1 | Control, no pathogen sequences |
| Lipofectamine MessengerMAX | BSL-1 | Chemical transfection reagent |
| Dopamine HCl | BSL-1 | Standard laboratory chemical |
| E. coli (NEB 10-beta) | BSL-1 | Non-pathogenic laboratory strain |


| Item | Supplier | Catalogue Number |
| --- | --- | --- |
| PC12 (CRL-1721) | ATCC | CRL-1721 |
| Note: standard PC12 not Adh variant — responds to NGF |  |  |


| Construct | Vector | Purpose |
| --- | --- | --- |
| rNurr1_mRNA_template_FINAL | pTwist Amp High Copy | IVT template for Nurr1 mRNA |
| rFoxA2_mRNA_template_FINAL | pTwist Amp High Copy | IVT template for FoxA2 mRNA |
| GFP_mRNA_template_FINAL | pTwist Amp High Copy | IVT template for GFP control mRNA |


| Reagent | Supplier | Notes |
| --- | --- | --- |
| DMEM high glucose with L-glutamine | Thermo Fisher | Base medium |
| Horse serum, heat inactivated | Thermo Fisher | 10% for growth, 1% for differentiation |
| FBS, heat inactivated | Thermo Fisher | 5% for growth |
| Penicillin/Streptomycin | Thermo Fisher | 100 U/ml + 100 µg/ml |
| PBS | Thermo Fisher | Sterile, calcium/magnesium-free |
| NGF 2.5S | Alomone / Sigma | 50-100 ng/ml working; store -80°C in BSA |
| BSA | Sigma | 0.1% for NGF dilution and storage |
| Poly-D-Lysine (PDL) | Sigma | 50 µg/ml coating solution |
| Laminin | Sigma/Thermo | 10 µg/ml coating solution |
| LB Broth | Sigma | Bacterial culture |
| LB Agar | Sigma | Bacterial plating |
| Ampicillin | Sigma | 100 µg/ml for bacterial selection |
| Lipofectamine MessengerMAX | Thermo Fisher | mRNA-specific transfection reagent |
| Opti-MEM | Thermo Fisher | For transfection complex preparation |
| HiScribe T7 High Yield RNA Synthesis Kit | NEB | IVT reaction |
| E. coli Poly-A Polymerase | NEB M0276 | Post-IVT poly-A addition |
| RNeasy Mini Kit | Qiagen | mRNA purification |
| RNase-free water | Thermo Fisher | All RNA steps |
| RNase inhibitor | NEB / Thermo | mRNA protection |
| KpnI restriction enzyme | NEB | Template linearisation |
| CutSmart Buffer | NEB | KpnI digestion |
| GeneJET Miniprep Kit | Thermo Fisher | Plasmid extraction |
| Dopamine hydrochloride | Sigma | Prepare fresh; store -80°C |
| Ascorbic acid | Sigma | 0.1 mg/ml; prevents dopamine oxidation |
| Dopamine ELISA Kit (High Sensitivity) | Eagle Biosciences / Abcam | Quantitative dopamine readout |
| Anti-TH antibody | Abcam | For differentiation verification |
| Secondary antibody (FITC) | Abcam | Fluorescent detection |
| DAPI | Thermo Fisher | Nuclear counterstain |
| 4% Paraformaldehyde | Sigma | Cell fixation |
| Triton X-100 | Sigma | Cell permeabilisation |
| Competent E. coli NEB 10-beta | NEB | Transformation |


| Component | Volume |
| --- | --- |
| RNase-free water | To 20 µl total |
| 10x T7 RNA Polymerase Buffer | 2 µl |
| ATP (100 mM) | 2 µl |
| CTP (100 mM) | 2 µl |
| GTP (100 mM) | 2 µl |
| UTP (100 mM) | 2 µl |
| Linearised DNA template (1 µg/µl) | 1 µl |
| T7 RNA Polymerase Mix | 2 µl |


| Day | Expected Observation |
| --- | --- |
| 9 | Cells healthy, attached, no visible neurites |
| 10-11 | Short process initiation visible |
| 12-13 | Clear neurite extensions 1-2 cell lengths |
| 14-15 | Mature neurite network, differentiation established |


| Well | Condition | mRNA transfected | Purpose |
| --- | --- | --- | --- |
| A1 | Negative control | None | Baseline PC12 |
| A2 | Transfection control | GFP mRNA only | Confirms transfection efficiency |
| A3 | Experimental | Nurr1 + FoxA2 mRNA | Dopaminergic differentiation |
| A4 | Experimental replicate | Nurr1 + FoxA2 mRNA | Technical replicate |
| A5 | Single factor control | Nurr1 mRNA only | Shows FoxA2 requirement |
| A6 | Single factor control | FoxA2 mRNA only | Shows Nurr1 requirement |


| Day | Action |
| --- | --- |
| 15 | First transfection — all wells |
| 16 | Second transfection + first TH staining check (optional) |
| 17 | Third transfection |
| 18 | Fourth transfection + first dopamine ELISA |
| 19 | Fifth transfection |
| 20 | Final transfection + TH immunostaining |
| 21 | Chemical RL experiment |


| Condition | Expected dopamine (relative) |
| --- | --- |
| Negative control (no mRNA) | Baseline (low) |
| GFP control | Similar to negative control |
| Nurr1 only | Modest increase over baseline |
| FoxA2 only | Modest increase over baseline |
| Nurr1 + FoxA2 | Significantly elevated — 3-5x baseline (Kim et al.) |


| Deck Position | Labware | Contents |
| --- | --- | --- |
| 1 | NEST 12-well reservoir | Dopamine working solutions (1, 5, 10 µM) + PBS wash |
| 2 | Corning 96-well flat plate | PC12 cells (transferred from 6-well) |
| 3 | Opentrons 96 tip rack (300 µl) | Standard tips |
| 4 | NEST 12-well reservoir (waste) | Aspirate waste |