diff --git a/tutorials/spherex/spherex_intro.md b/tutorials/spherex/spherex_intro.md
index ed3caeea..b5d3c676 100644
--- a/tutorials/spherex/spherex_intro.md
+++ b/tutorials/spherex/spherex_intro.md
@@ -7,9 +7,9 @@ jupytext:
     format_version: 0.13
     jupytext_version: 1.17.2
 kernelspec:
-  name: python3
   display_name: Python 3 (ipykernel)
   language: python
+  name: python3
 ---
 
 # Introduction to SPHEREx Spectral Images
@@ -58,7 +58,7 @@ from astropy.table import Table
 from astropy import units as u
 from astropy.coordinates import SkyCoord
 
-import pyvo
+from astroquery.ipac.irsa import Irsa
 from pyvo.dal.adhoc import DatalinkResults
 
 from firefly_client import FireflyClient
@@ -73,31 +73,31 @@ Define some coordinates of interest.
 ```{code-cell} ipython3
 ra_deg = 304.693508808
 dec_deg = 42.4436872991
+
+coord = SkyCoord(ra_deg, dec_deg, unit='deg')
+search_radius = 1 * u.arcsec
 ```
 
-Query IRSA for a list of Spectral Image MEFs that overlap this position.
+Query IRSA for a list of Spectral Image MEFs that overlap this position. We use the [IRSA module in astroquery](https://astroquery.readthedocs.io/en/latest/ipac/irsa/irsa.html) and the Simple Image Access API.
 
-```{code-cell} ipython3
-# Define the TAP service URL for IRSA
-tap_url = "https://irsa.ipac.caltech.edu/TAP"
++++
 
-# Connect to the TAP service
-service = pyvo.dal.TAPService(tap_url)
+```{tip}
+The IRSA SIA collections can be listed using using the ``list_collections`` method, we can filter on the ones containing "spherex" in the collection name:
 
-# Define your ADQL query
-query = "SELECT * FROM spherex.obscore WHERE CONTAINS(POINT('ICRS',"+str(ra_deg)+","+str(dec_deg)+"), s_region)=1"
+    Irsa.list_collections(filter='spherex')
+```
 
-# Submit the asynchronous query
-job = service.submit_job(query)
++++
 
-# Run the job (starts the query execution on the server)
-job.run()
+There are currently three collections available:
 
-# Wait for the job to complete (polling)
-job.wait(phases=["COMPLETED", "ERROR", "ABORTED"], timeout=300)
+* `'spherex_qr'` -- the Quick Release Observations, for more information see [SPHEREx archive documentation at IRSA]().
+* `'spherex_qr_cal'` -- the Quick Release Calibration products.
+* `'spherex_qr_deep'` -- 
 
-# Capture the results
-results = job.fetch_result()
+```{code-cell} ipython3
+results = Irsa.query_sia(pos=(coord, search_radius), collection='spherex_qr')
 ```
 
 Each row of the results of your query represents a different spectral image.
@@ -112,10 +112,10 @@ len(results)
 The query results provide a lot of metadata about each spectral image. These columns have standard names as defined by the IVOA. Let's list them:
 
 ```{code-cell} ipython3
-list(results.fieldnames)
+results.colnames
 ```
 
-The 'access_url' column is particularly important because it tells you how to access the data. Let's look at the 'access_url' value for the first row:
+The `'access_url'` column is particularly important because it tells you how to access the data. Let's look at the `'access_url'` value for the first row:
 
 ```{code-cell} ipython3
 results['access_url'][0]